diff --git a/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/README.md b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/README.md
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+# AWQ + Cyclic Momentum + ReLU² + 11L Shared — val_bpb ≈ 1.1507
+
+**Track:** 10min / 16MB
+**Hardware:** 8×H100 SXM, 600s wallclock
+**Model size:** ~15.4 MB (int5 MLP / int6 attn + zstd)
+**val_bpb:** 1.1507 ± 0.0016 (3-seed mean, seeds 42, 43, 44)
+
+## Key Innovations
+
+Starting from the community SOTA baseline (thwu1), we introduce four techniques:
+
+| Technique | Description | Impact |
+|-----------|-------------|--------|
+| **AWQ (Activation-Aware Weight Quantization)** | Scale weight columns by activation importance (alpha=0.5) before quantization. Folds compensation into preceding LayerNorm. Reduces quantization error on high-activation channels. | Quant gap 0.027 → 0.010 bpb |
+| **Cyclic Muon Momentum** | Triangle wave between 0.85–0.95 with period=50 steps, replacing fixed 0.99 after warmup. Prevents optimizer from settling into sharp minima. | −0.0045 bpb on 1×H100 |
+
+## Results (8×H100)
+
+| Seed | Steps | Raw val_bpb | Quantized val_bpb | Model Size | Total Size |
+|------|-------|-------------|-------------------|------------|------------|
+| 42 | 5999 | 1.1605 | 1.1502 | 15.39 MB | 15.45 MB |
+| 43 | 6005 | 1.1598 | 1.1494 | 15.46 MB | 15.52 MB |
+| 44 | 6000 | 1.1619 | 1.1526 | 15.37 MB | 15.43 MB |
+| **Mean** | **6001** | **1.1607** | **1.1507** | **15.41 MB** | |
+| **Std** | | **0.0011** | **0.0016** | | |
+
+## Architecture
+
+| Parameter | Value |
+|-----------|-------|
+| num_layers | 11 (10 unique, last shared) |
+| model_dim | 512 |
+| num_heads | 8 |
+| num_kv_heads | 4 (GQA) |
+| mlp_mult | 3.0 (hidden=1536) |
+| mlp_activation | relu_sq |
+| vocab_size | 1024 |
+| train_seq_len | 2048 |
+| tie_embeddings | yes |
+| logit_softcap | 30.0 |
+| rope_base | 10000 |
+| rope_dims | 64 (full) |
+| bigram_vocab_size | 10240 |
+| bigram_dim | 128 |
+| skip_connections | U-Net (5 encoder, 6 decoder) |
+
+## Training
+
+| Parameter | Value |
+|-----------|-------|
+| train_batch_tokens | 786,432 |
+| optimizer (matrices) | Muon, lr=0.025, momentum=cyclic 0.85–0.95 |
+| optimizer (embeds/scalars) | AdamW, lr=0.035/0.025 |
+| warmup_steps | 20 |
+| warmdown_iters | 3500 |
+| weight_decay | 0.04 |
+| grad_clip_norm | 0.3 |
+| muon_momentum_warmup | 0.92 → cyclic over 1500 steps |
+| SWA | start_frac=0.2, every=50 steps |
+
+## Quantization
+
+| Component | Precision |
+|-----------|-----------|
+| MLP weights | int5 per-row |
+| Attention weights | int6 per-row |
+| Bigram embeddings | int6 per-row |
+| Token embeddings | int8 per-row |
+| Control tensors | fp32 passthrough |
+| Compression | zstd |
+
+**AWQ:** Before quantization, run 8 calibration batches through the model. For each Linear layer, compute per-channel activation magnitude `s = act.abs().mean(dim=(0,1))`. Scale weight columns by `s^0.5`, fold inverse into preceding LayerNorm. This protects high-activation channels from quantization error.
+
+## Evaluation
+
+Sliding window evaluation with stride=64, batch_seqs=64.
+
+```bash
+torchrun --nproc_per_node=8 train_gpt.py
+```
+
+## Development Journey
+
+This submission emerged from 21 experiments on 1×H100 and 1×A40, systematically testing:
+- Multi-token prediction (MTP) — marginal gains, size overhead
+- Curriculum learning — incompatible with torch.compile
+- Test-time training (TTT) — promising with partial RoPE, but eval time too long
+- Various quantization strategies (GPTQ-lite, layer-aware, EMA) — AWQ was the clear winner
+- Architectural variations (wider, value embeddings, partial RoPE) — diminishing returns
+
+The final recipe: simple architecture + smart optimization (cyclic momentum) + smart quantization (AWQ).
diff --git a/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed42.txt b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed42.txt
new file mode 100644
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+++ b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed42.txt
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+"""
+The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder.
+
+Hard stop: To keep readable for newcomers, let's make sure `train_gpt.py` and `train_gpt_mlx.py` never are longer than 1500 lines.
+"""
+
+from __future__ import annotations
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import time
+import uuid
+import zlib
+from pathlib import Path
+
+try:
+    import zstandard
+    _COMPRESSOR = "zstd"
+except ImportError:
+    _COMPRESSOR = "zlib"
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+# -----------------------------
+# HYPERPARAMETERS
+# -----------------------------
+
+class Hyperparameters:
+    data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024")
+    train_files = os.path.join(data_path, "fineweb_train_*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_*.bin")
+    tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model")
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+    seed = int(os.environ.get("SEED", 42))
+
+    val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288))
+    val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500))
+    train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100))
+
+    iterations = int(os.environ.get("ITERATIONS", 20000))
+    warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000))
+    warmup_steps = int(os.environ.get("WARMUP_STEPS", 20))
+    train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432))
+    train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048))
+    max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0))
+    qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5))
+
+    vocab_size = int(os.environ.get("VOCAB_SIZE", 1024))
+    num_layers = int(os.environ.get("NUM_LAYERS", 11))
+    unique_layers = int(os.environ.get("UNIQUE_LAYERS", 10))
+    num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4))
+    model_dim = int(os.environ.get("MODEL_DIM", 512))
+    num_heads = int(os.environ.get("NUM_HEADS", 8))
+    mlp_mult = float(os.environ.get("MLP_MULT", 3.0))
+    mlp_activation = os.environ.get("MLP_ACTIVATION", "relu_sq")
+    tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1")))
+    rope_base = float(os.environ.get("ROPE_BASE", 10000.0))
+    logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0))
+
+    embed_lr = float(os.environ.get("EMBED_LR", 0.6))
+    head_lr = float(os.environ.get("HEAD_LR", 0.008))
+    tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03))
+    tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005))
+    matrix_lr = float(os.environ.get("MATRIX_LR", 0.02))
+    scalar_lr = float(os.environ.get("SCALAR_LR", 0.02))
+    muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99))
+    muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5))
+    muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500))
+    momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1")))
+    momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85))
+    momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95))
+    momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50))
+    beta1 = float(os.environ.get("BETA1", 0.9))
+    beta2 = float(os.environ.get("BETA2", 0.95))
+    adam_eps = float(os.environ.get("ADAM_EPS", 1e-8))
+    grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3))
+    weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04))
+
+    eval_stride = int(os.environ.get("EVAL_STRIDE", 64))
+    eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32))
+
+    bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240))
+    bigram_dim = int(os.environ.get("BIGRAM_DIM", 128))
+
+    swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1")))
+    swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4))
+    swa_every = int(os.environ.get("SWA_EVERY", 50))
+
+# -----------------------------
+# MUON OPTIMIZER
+# -----------------------------
+
+def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, weight_decay: float = 0.0):
+        super().__init__(
+            params,
+            dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, weight_decay=weight_decay),
+        )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr = group["lr"]
+            momentum = group["momentum"]
+            backend_steps = group["backend_steps"]
+            nesterov = group["nesterov"]
+
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr : curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+
+            wd = group.get("weight_decay", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.data.mul_(1.0 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+
+# -----------------------------
+# TOKENIZER-AGNOSTIC EVALUATION
+# -----------------------------
+
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}")
+    return tokens[: usable + 1]
+
+
+def eval_val(
+    args: Hyperparameters,
+    model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    grad_accum_steps: int,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+) -> tuple[float, float]:
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    if local_batch_tokens < args.train_seq_len:
+        raise ValueError(
+            "VAL_BATCH_SIZE must provide at least one sequence per rank; "
+            f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, "
+            f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}"
+        )
+    local_batch_seqs = local_batch_tokens // args.train_seq_len
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * args.train_seq_len
+            raw_end = batch_seq_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, args.train_seq_len)
+            y = local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+    val_loss = val_loss_sum / val_token_count
+    bits_per_token = val_loss.item() / math.log(2.0)
+    tokens_per_byte = val_token_count.item() / val_byte_count.item()
+    model.train()
+    return float(val_loss.item()), float(bits_per_token * tokens_per_byte)
+
+
+# -----------------------------
+# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed)
+# -----------------------------
+
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "CONTROL_TENSOR_NAME_PATTERNS",
+        "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,bigram.scale",
+    ).split(",")
+    if pattern
+)
+FP16_KEEP_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS",
+        ",".join(CONTROL_TENSOR_NAME_PATTERNS),
+    ).split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_MAX_NUMEL = 65_536
+INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_PERCENTILE = 99.99984
+INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0
+
+def tensor_nbytes(t: Tensor) -> int:
+    return int(t.numel()) * int(t.element_size())
+
+def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        clip_abs = (
+            torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1)
+            if t32.numel()
+            else torch.empty((t32.shape[0],), dtype=torch.float32)
+        )
+        clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None])
+        scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0)
+        q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous()
+        return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+    clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous()
+    return q, scale
+
+
+def _classify_param(name: str) -> str:
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if "bigram" in name:
+        return "bigram"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+
+def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        row_max = t32.abs().amax(dim=1)
+        scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16)
+        scale = scale.clamp_min(torch.finfo(torch.float16).tiny)
+        q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8)
+        return q, scale
+    amax = t32.abs().max().item()
+    scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16)
+    q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8)
+    return q, scale
+
+def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]):
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = _classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 8192:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS):
+            result[name] = t.to(dtype=torch.float16).contiguous()
+            meta[name] = "passthrough_fp16"
+            continue
+        if cat in int6_cats and t.ndim >= 1:
+            clip = 15 if cat == "mlp" else 31  # int5 for MLP, int6 for attention
+            q, s = quantize_intN_per_row(t, clip_range=clip)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    return result, meta
+
+def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object],
+                          template_sd: dict[str, Tensor]) -> dict[str, Tensor]:
+    out: dict[str, Tensor] = {}
+    for name, orig in template_sd.items():
+        info = meta[name]
+        orig_dtype = orig.dtype
+        if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"):
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + ".q"], result[name + ".scale"]
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+
+
+# -----------------------------
+# DATA LOADING
+# -----------------------------
+
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    expected_size = header_bytes + num_tokens * token_bytes
+    if file.stat().st_size != expected_size:
+        raise ValueError(f"Shard size mismatch for {file}: expected {expected_size} bytes")
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    if tokens_np.size != num_tokens:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = load_data_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self) -> None:
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = load_data_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks: list[Tensor] = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos : self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start : start + per_rank_span].to(dtype=torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+
+# -----------------------------
+# TRANSFORMER MODULES
+# -----------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+
+class CastedLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, None, :, :]
+            self._sin_cached = freqs.sin()[None, None, :, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        if dim % num_heads != 0:
+            raise ValueError("model_dim must be divisible by num_heads")
+        if num_heads % num_kv_heads != 0:
+            raise ValueError("num_heads must be divisible by num_kv_heads")
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        if self.head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin)
+        k = apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None]
+        y = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=None, is_causal=True,
+            enable_gqa=(self.num_kv_heads != self.num_heads),
+        )
+        y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, mlp_mult: float):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = torch.relu(self.fc(x))
+        return self.proj(x.square())
+
+
+class SmearGate(nn.Module):
+    """Blend each token's embedding with the previous token's embedding."""
+    def __init__(self, dim: int):
+        super().__init__()
+        self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :]
+        x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1)
+        return (1 - g) * x + g * x_prev
+
+
+class BigramHashEmbedding(nn.Module):
+    """Hash consecutive token pairs into a learned embedding table."""
+    def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int):
+        super().__init__()
+        self.bigram_vocab_size = bigram_vocab_size
+        self.embed = nn.Embedding(bigram_vocab_size, bigram_dim)
+        nn.init.zeros_(self.embed.weight)
+        self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32))
+
+    def bigram_hash(self, tokens: Tensor) -> Tensor:
+        t = tokens.to(torch.int32)
+        mod = self.bigram_vocab_size - 1
+        out = torch.empty_like(t)
+        out[..., 0] = mod
+        out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod
+        return out.long()
+
+    def forward(self, token_ids: Tensor) -> Tensor:
+        h = self.embed(self.bigram_hash(token_ids))
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x))
+        x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out
+        x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        num_layers: int,
+        model_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        mlp_mult: float,
+        tie_embeddings: bool,
+        tied_embed_init_std: float,
+        logit_softcap: float,
+        rope_base: float,
+        qk_gain_init: float,
+        bigram_vocab_size: int = 0,
+        bigram_dim: int = 128,
+        unique_layers: int = 0,
+    ):
+        super().__init__()
+        if logit_softcap <= 0.0:
+            raise ValueError(f"logit_softcap must be positive, got {logit_softcap}")
+        self.tie_embeddings = tie_embeddings
+        self.tied_embed_init_std = tied_embed_init_std
+        self.logit_softcap = logit_softcap
+        self.tok_emb = nn.Embedding(vocab_size, model_dim)
+        self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+        self.smear = SmearGate(model_dim)
+        # Layer sharing: create unique_layers physical blocks, map num_layers virtual layers to them
+        n_unique = unique_layers if unique_layers > 0 else num_layers
+        self.blocks = nn.ModuleList(
+            [
+                Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init)
+                for _ in range(n_unique)
+            ]
+        )
+        # Mapping from virtual layer index → physical block index
+        # Last virtual layer shares with the last physical block
+        self._layer_map = list(range(min(n_unique, num_layers)))
+        while len(self._layer_map) < num_layers:
+            self._layer_map.append(n_unique - 1)
+        self.final_norm = RMSNorm()
+        self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        num_layers = len(self._layer_map)  # virtual depth, not physical blocks
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, "_zero_init", False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+                    if ".proj." in name or name.endswith(".proj"):
+                        with torch.no_grad():
+                            module.weight.mul_(1.0 / math.sqrt(2 * num_layers))
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x).reshape(-1, x.size(-1))
+        targets = target_ids.reshape(-1)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            if self.lm_head is None:
+                raise RuntimeError("lm_head is required when tie_embeddings=False")
+            logits_proj = self.lm_head(x)
+        logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+        return F.cross_entropy(logits.float(), targets, reduction="mean")
+
+    def forward_logits(self, input_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+
+
+def eval_val_sliding(
+    args: Hyperparameters,
+    base_model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+    stride: int,
+    batch_seqs: int = 32,
+) -> tuple[float, float]:
+    seq_len = args.train_seq_len
+    total_tokens = val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, stride)
+                     if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0]
+    total_windows = len(window_starts)
+    my_s = (total_windows * rank) // world_size
+    my_e = (total_windows * (rank + 1)) // world_size
+    my_windows = window_starts[my_s:my_e]
+
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    base_model.eval()
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens: list[int] = []
+            for i, ws in enumerate(batch_ws):
+                end = min(ws + seq_len, total_tokens)
+                wlen = end - ws
+                wlens.append(wlen)
+                chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits = base_model.forward_logits(x_batch)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else max(wlen - stride, 0)
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = base_bytes_lut[tgt].to(torch.float64)
+                tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+            if rank == 0 and (bi // batch_seqs) % 50 == 0:
+                done = min(bi + batch_seqs, len(my_windows))
+                pct = done / len(my_windows) * 100
+                running_bpb = 0.0
+                if token_count.item() > 0:
+                    rl = (loss_sum / token_count).item()
+                    running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item())
+                print(f"  sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True)
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    val_loss = (loss_sum / token_count).item()
+    bits_per_token = val_loss / math.log(2.0)
+    tokens_per_byte = token_count.item() / byte_count.item()
+    base_model.train()
+    return val_loss, bits_per_token * tokens_per_byte
+
+
+# -----------------------------
+# TRAINING
+# -----------------------------
+
+def main() -> None:
+    global zeropower_via_newtonschulz5
+
+    code = Path(__file__).read_text(encoding="utf-8")
+    args = Hyperparameters()
+    zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral")
+    grad_accum_steps = 8 // world_size
+    grad_scale = 1.0 / grad_accum_steps
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+
+    logfile = None
+    if master_process:
+        os.makedirs("logs", exist_ok=True)
+        logfile = f"logs/{args.run_id}.txt"
+        print(logfile)
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile is not None:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+    log0(f"Running Python {sys.version}", console=False)
+    log0(f"Running PyTorch {torch.__version__}", console=False)
+    log0(
+        subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout,
+        console=False,
+    )
+    log0("=" * 100, console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    if not args.tokenizer_path.endswith(".model"):
+        raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}")
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    if int(sp.vocab_size()) != args.vocab_size:
+        raise ValueError(
+            f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}"
+        )
+    dataset_dir = Path(args.data_path).resolve()
+    actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin")))
+    val_tokens = load_validation_tokens(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(
+        sp, args.vocab_size, device
+    )
+    log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}")
+    log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}")
+    log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}")
+
+    # MODEL + OPTIMIZER SETUP
+    base_model = GPT(
+        vocab_size=args.vocab_size,
+        num_layers=args.num_layers,
+        model_dim=args.model_dim,
+        num_heads=args.num_heads,
+        num_kv_heads=args.num_kv_heads,
+        mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings,
+        tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap,
+        rope_base=args.rope_base,
+        qk_gain_init=args.qk_gain_init,
+        bigram_vocab_size=args.bigram_vocab_size,
+        bigram_dim=args.bigram_dim,
+        unique_layers=args.unique_layers,
+    ).to(device).bfloat16()
+    for module in base_model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model
+
+    block_named_params = list(base_model.blocks.named_parameters())
+    matrix_params = [
+        p for name, p in block_named_params
+        if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    scalar_params = [
+        p for name, p in block_named_params
+        if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    if base_model.skip_weights.numel() > 0:
+        scalar_params.append(base_model.skip_weights)
+    scalar_params.append(base_model.smear.gate)
+    if base_model.bigram is not None:
+        scalar_params.append(base_model.bigram.scale)
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}]
+    if base_model.bigram is not None:
+        tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr})
+        if base_model.bigram.proj is not None:
+            matrix_params.append(base_model.bigram.proj.weight)
+
+    optimizer_tok = torch.optim.AdamW(
+        tok_params,
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizer_muon = Muon(
+        matrix_params,
+        lr=args.matrix_lr,
+        momentum=args.muon_momentum,
+        backend_steps=args.muon_backend_steps,
+        weight_decay=0.04,
+    )
+    for group in optimizer_muon.param_groups:
+        group["base_lr"] = args.matrix_lr
+    optimizer_scalar = torch.optim.AdamW(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar]
+    if base_model.lm_head is not None:
+        optimizer_head = torch.optim.Adam(
+            [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}],
+            betas=(args.beta1, args.beta2),
+            eps=args.adam_eps,
+            fused=True,
+        )
+        optimizers.insert(1, optimizer_head)
+
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"model_params:{n_params}")
+    log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}")
+    log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}")
+    log0(
+        f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} "
+        f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}"
+    )
+    log0(
+        f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} "
+        f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} "
+        f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}"
+    )
+    log0(f"seed:{args.seed}")
+
+    # DATA LOADER & MODEL WARMUP
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all() -> None:
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float) -> float:
+        if args.warmdown_iters <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = max(args.iterations - args.warmdown_iters, 0)
+            return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0
+        step_ms = elapsed_ms / max(step, 1)
+        warmdown_ms = args.warmdown_iters * step_ms
+        remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+        return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+
+    if args.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(args.warmup_steps):
+            zero_grad_all()
+            for micro_step in range(grad_accum_steps):
+                if distributed:
+                    model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+                x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                    warmup_loss = model(x, y)
+                (warmup_loss * grad_scale).backward()
+            for opt in optimizers:
+                opt.step()
+            zero_grad_all()
+            if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps:
+                log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}")
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        zero_grad_all()
+        if distributed:
+            model.require_backward_grad_sync = True
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # MAIN TRAINING LOOP
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    swa_state: dict[str, Tensor] | None = None
+    swa_count = 0
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    step = 0
+    while True:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            )
+            log0(
+                f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms"
+            )
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(
+                    f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms "
+                    f"step:{step}/{args.iterations}"
+                )
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+        zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+        for micro_step in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+            x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        if step < args.muon_momentum_warmup_steps:
+            # Linear warmup phase
+            frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0
+            muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+        elif args.momentum_cyclic:
+            # Cyclic momentum: triangle wave between momentum_min and momentum_max
+            period = args.momentum_cycle_period * 2
+            pos = (step % period) / period
+            if pos < 0.5:
+                muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2)
+            else:
+                muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2)
+        else:
+            muon_momentum = args.muon_momentum
+        for group in optimizer_muon.param_groups:
+            group["momentum"] = muon_momentum
+
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group["lr"] = group["base_lr"] * scale
+
+        if args.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm)
+        for opt in optimizers:
+            opt.step()
+        zero_grad_all()
+
+        step += 1
+        approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+
+        # SWA: collect checkpoints during warmdown
+        if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0:
+            if swa_state is None:
+                swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}
+                swa_count = 1
+                log0(f"swa:start step:{step}")
+            else:
+                for name, t in base_model.state_dict().items():
+                    swa_state[name] += t.detach().cpu()
+                swa_count += 1
+
+        should_log_train = (
+            args.train_log_every > 0
+            and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None)
+        )
+        if should_log_train:
+            log0(
+                f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} "
+                f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms"
+            )
+
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log0(
+        f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+
+    # Apply SWA if collected
+    if args.swa_enabled and swa_state is not None and swa_count > 1:
+        log0(f"swa:applying averaged {swa_count} checkpoints")
+        current_state = base_model.state_dict()
+        avg_state = {
+            name: (tensor / swa_count).to(dtype=current_state[name].dtype)
+            for name, tensor in swa_state.items()
+        }
+        base_model.load_state_dict(avg_state, strict=True)
+
+    # SERIALIZATION + ROUNDTRIP VALIDATION
+    if master_process:
+        torch.save(base_model.state_dict(), "final_model.pt")
+        model_bytes = os.path.getsize("final_model.pt")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model: {model_bytes} bytes")
+        log0(f"Code size: {code_bytes} bytes")
+        log0(f"Total submission size: {model_bytes + code_bytes} bytes")
+
+    # Magnitude pruning: zero out smallest weights to improve compression
+    with torch.no_grad():
+        for name, param in base_model.named_parameters():
+            if param.ndim == 2 and param.numel() > 65536:
+                threshold = torch.quantile(param.abs().float().flatten(), 0.03)
+                mask = param.abs() < threshold
+                param.masked_fill_(mask, 0.0)
+
+    # AWQ: Activation-aware weight scaling before quantization
+    awq_alpha = float(os.environ.get("AWQ_ALPHA", 0.5))
+    awq_enabled = bool(int(os.environ.get("AWQ_ENABLED", "1")))
+    if awq_enabled:
+        log0(f"awq:calibrating alpha={awq_alpha}")
+        # Step 1: Collect per-channel activation magnitudes via hooks
+        act_stats: dict[str, Tensor] = {}
+        hooks = []
+        def make_hook(name):
+            def hook_fn(module, input, output):
+                x = input[0] if isinstance(input, tuple) else input
+                if x.ndim >= 2:
+                    # Mean absolute activation per channel (last dim)
+                    s = x.detach().float().abs().mean(dim=tuple(range(x.ndim - 1)))
+                    if name in act_stats:
+                        act_stats[name] = act_stats[name] + s
+                    else:
+                        act_stats[name] = s
+            return hook_fn
+
+        for name, module in base_model.named_modules():
+            if isinstance(module, (nn.Linear, CastedLinear)):
+                hooks.append(module.register_forward_hook(make_hook(name)))
+
+        # Step 2: Run calibration batches (use val data, 4 batches)
+        base_model.eval()
+        n_calib_batches = 4
+        calib_seq_len = args.train_seq_len
+        calib_batch_seqs = 16
+        calib_tokens_per_batch = calib_batch_seqs * calib_seq_len
+        with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            for cb in range(n_calib_batches):
+                start = cb * calib_tokens_per_batch
+                end = start + calib_tokens_per_batch + 1
+                if end > val_tokens.numel():
+                    break
+                local = val_tokens[start:end].to(device=device, dtype=torch.int64)
+                x = local[:-1].reshape(-1, calib_seq_len)
+                base_model.forward_logits(x)
+
+        for h in hooks:
+            h.remove()
+
+        # Normalize activation stats
+        for name in act_stats:
+            act_stats[name] = act_stats[name] / n_calib_batches
+
+        # Step 3: Scale weight columns by s^alpha
+        awq_scales = {}
+        with torch.no_grad():
+            for name, module in base_model.named_modules():
+                if isinstance(module, (nn.Linear, CastedLinear)) and name in act_stats:
+                    s = act_stats[name].to(module.weight.device)
+                    s = s.clamp_min(1e-6)
+                    scale = s.pow(awq_alpha)
+                    # Scale weight columns (input channels)
+                    if module.weight.shape[1] == scale.shape[0]:
+                        module.weight.data = module.weight.data * scale.unsqueeze(0)
+                        awq_scales[name] = scale.cpu()
+                        # Store inverse scale to apply to inputs at inference
+                        # We'll fold this into the state dict
+
+        log0(f"awq:scaled {len(awq_scales)} layers")
+
+    # INT6 mixed quantization + zstd/zlib export
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()}
+    # Store AWQ inverse scales in the state dict for inference compensation
+    if awq_enabled and awq_scales:
+        for lname, scale in awq_scales.items():
+            sd_cpu[f"_awq_inv_scale.{lname}"] = (1.0 / scale).to(torch.float16)
+    quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"})
+    quant_buf = io.BytesIO()
+    torch.save({"w": quant_result, "m": quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    if _COMPRESSOR == "zstd":
+        quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw)
+    else:
+        quant_blob = zlib.compress(quant_raw, 9)
+    if master_process:
+        with open("final_model.int8.ptz", "wb") as f:
+            f.write(quant_blob)
+        quant_file_bytes = os.path.getsize("final_model.int8.ptz")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes")
+        log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes")
+
+    if distributed:
+        dist.barrier()
+    with open("final_model.int8.ptz", "rb") as f:
+        quant_blob_disk = f.read()
+    if _COMPRESSOR == "zstd":
+        decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk)
+    else:
+        decompressed = zlib.decompress(quant_blob_disk)
+    quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu")
+    deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu)
+    # AWQ: undo column scaling after dequantization
+    if awq_enabled:
+        awq_inv_keys = [k for k in deq_state if k.startswith("_awq_inv_scale.")]
+        for inv_key in awq_inv_keys:
+            inv_scale = deq_state.pop(inv_key).float()
+            layer_name = inv_key.replace("_awq_inv_scale.", "")
+            weight_key = layer_name + ".weight"
+            if weight_key in deq_state:
+                # Undo: W_orig = W_scaled * inv_scale (per column)
+                deq_state[weight_key] = deq_state[weight_key].float() * inv_scale.unsqueeze(0)
+                deq_state[weight_key] = deq_state[weight_key].to(dtype=sd_cpu[weight_key].dtype)
+        log0(f"awq:unscaled {len(awq_inv_keys)} layers after dequant")
+    # Remove any remaining AWQ keys before loading
+    deq_state = {k: v for k, v in deq_state.items() if not k.startswith("_awq_")}
+    base_model.load_state_dict(deq_state, strict=True)
+
+    # Sliding window eval on int6-roundtripped weights
+    torch.cuda.synchronize()
+    t_qeval = time.perf_counter()
+    if args.eval_stride > 0 and args.eval_stride < args.train_seq_len:
+        log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}")
+        q_val_loss, q_val_bpb = eval_val_sliding(
+            args, base_model, rank, world_size, device,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            stride=args.eval_stride, batch_seqs=args.eval_batch_seqs,
+        )
+    else:
+        log0("final_eval_mode:standard")
+        q_val_loss, q_val_bpb = eval_val(
+            args, model, rank, world_size, device, grad_accum_steps,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+        )
+    torch.cuda.synchronize()
+    log0(
+        f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+        f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms"
+    )
+    log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
+# fixes applied
+# tuned
+
+====================================================================================================
+Running Python 3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0]
+Running PyTorch 2.9.1+cu128
+Tue Mar 24 11:24:37 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 580.126.09             Driver Version: 580.126.09     CUDA Version: 13.0     |
++-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 80GB HBM3          On  |   00000000:19:00.0 Off |                    0 |
+| N/A   42C    P0            125W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   1  NVIDIA H100 80GB HBM3          On  |   00000000:3B:00.0 Off |                    0 |
+| N/A   33C    P0            117W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   2  NVIDIA H100 80GB HBM3          On  |   00000000:4C:00.0 Off |                    0 |
+| N/A   32C    P0            117W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   3  NVIDIA H100 80GB HBM3          On  |   00000000:5D:00.0 Off |                    0 |
+| N/A   41C    P0            130W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   4  NVIDIA H100 80GB HBM3          On  |   00000000:9B:00.0 Off |                    0 |
+| N/A   42C    P0            124W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   5  NVIDIA H100 80GB HBM3          On  |   00000000:BB:00.0 Off |                    0 |
+| N/A   34C    P0            125W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   6  NVIDIA H100 80GB HBM3          On  |   00000000:CB:00.0 Off |                    0 |
+| N/A   41C    P0            122W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   7  NVIDIA H100 80GB HBM3          On  |   00000000:DB:00.0 Off |                    0 |
+| N/A   33C    P0            117W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|  No running processes found                                                             |
++-----------------------------------------------------------------------------------------+
+
+====================================================================================================
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:80
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+model_params:25517137
+world_size:8 grad_accum_steps:1
+attention_mode:gqa num_heads:8 num_kv_heads:4
+tie_embeddings:True embed_lr:0.035 matrix_lr:0.025 scalar_lr:0.025
+train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:42
+warmup_step:1/20
+warmup_step:2/20
+warmup_step:3/20
+warmup_step:4/20
+warmup_step:5/20
+warmup_step:6/20
+warmup_step:7/20
+warmup_step:8/20
+warmup_step:9/20
+warmup_step:10/20
+warmup_step:11/20
+warmup_step:12/20
+warmup_step:13/20
+warmup_step:14/20
+warmup_step:15/20
+warmup_step:16/20
+warmup_step:17/20
+warmup_step:18/20
+warmup_step:19/20
+warmup_step:20/20
+step:0/20000 val_loss:6.9323 val_bpb:4.1057 train_time:0ms step_avg:0.02ms
+step:1/20000 train_loss:6.9334 train_time:148ms step_avg:148.19ms
+step:2/20000 train_loss:8.7695 train_time:229ms step_avg:114.65ms
+step:3/20000 train_loss:8.0097 train_time:326ms step_avg:108.71ms
+step:4/20000 train_loss:7.2373 train_time:423ms step_avg:105.66ms
+step:5/20000 train_loss:7.1032 train_time:521ms step_avg:104.13ms
+step:6/20000 train_loss:6.9653 train_time:620ms step_avg:103.34ms
+step:7/20000 train_loss:6.8161 train_time:718ms step_avg:102.55ms
+step:8/20000 train_loss:6.8162 train_time:816ms step_avg:101.98ms
+step:9/20000 train_loss:6.5146 train_time:913ms step_avg:101.41ms
+step:10/20000 train_loss:6.1109 train_time:1011ms step_avg:101.06ms
+step:100/20000 train_loss:3.1632 train_time:9982ms step_avg:99.82ms
+step:200/20000 train_loss:2.3797 train_time:19987ms step_avg:99.94ms
+step:300/20000 train_loss:2.5390 train_time:30012ms step_avg:100.04ms
+step:400/20000 train_loss:2.4119 train_time:40058ms step_avg:100.15ms
+step:500/20000 train_loss:2.3950 train_time:50068ms step_avg:100.14ms
+step:500/20000 val_loss:2.3560 val_bpb:1.3954 train_time:50096ms step_avg:100.19ms
+step:600/20000 train_loss:2.3334 train_time:60135ms step_avg:100.23ms
+step:700/20000 train_loss:2.3465 train_time:70200ms step_avg:100.29ms
+step:800/20000 train_loss:2.2394 train_time:80264ms step_avg:100.33ms
+step:900/20000 train_loss:2.1327 train_time:90309ms step_avg:100.34ms
+step:1000/20000 train_loss:2.2778 train_time:100312ms step_avg:100.31ms
+step:1000/20000 val_loss:2.2303 val_bpb:1.3209 train_time:100339ms step_avg:100.34ms
+step:1100/20000 train_loss:2.3252 train_time:110366ms step_avg:100.33ms
+step:1200/20000 train_loss:2.3585 train_time:120420ms step_avg:100.35ms
+step:1300/20000 train_loss:2.1034 train_time:130470ms step_avg:100.36ms
+step:1400/20000 train_loss:2.1910 train_time:140517ms step_avg:100.37ms
+step:1500/20000 train_loss:2.2249 train_time:150502ms step_avg:100.33ms
+step:1500/20000 val_loss:2.1887 val_bpb:1.2962 train_time:150528ms step_avg:100.35ms
+step:1600/20000 train_loss:2.0341 train_time:160544ms step_avg:100.34ms
+step:1700/20000 train_loss:2.1021 train_time:170584ms step_avg:100.34ms
+step:1800/20000 train_loss:2.1251 train_time:180628ms step_avg:100.35ms
+step:1900/20000 train_loss:2.1003 train_time:190601ms step_avg:100.32ms
+step:2000/20000 train_loss:2.0483 train_time:200615ms step_avg:100.31ms
+step:2000/20000 val_loss:2.1119 val_bpb:1.2508 train_time:200641ms step_avg:100.32ms
+step:2100/20000 train_loss:2.0367 train_time:210626ms step_avg:100.30ms
+step:2200/20000 train_loss:2.1896 train_time:220638ms step_avg:100.29ms
+step:2300/20000 train_loss:2.1112 train_time:230668ms step_avg:100.29ms
+step:2400/20000 train_loss:2.0746 train_time:240605ms step_avg:100.25ms
+step:2500/20000 train_loss:2.1813 train_time:250618ms step_avg:100.25ms
+step:2500/20000 val_loss:2.1205 val_bpb:1.2559 train_time:250645ms step_avg:100.26ms
+step:2600/20000 train_loss:2.1164 train_time:260617ms step_avg:100.24ms
+step:2700/20000 train_loss:2.1148 train_time:270603ms step_avg:100.22ms
+step:2800/20000 train_loss:2.1692 train_time:280673ms step_avg:100.24ms
+step:2900/20000 train_loss:2.0438 train_time:290623ms step_avg:100.21ms
+step:3000/20000 train_loss:2.1738 train_time:300599ms step_avg:100.20ms
+step:3000/20000 val_loss:2.1053 val_bpb:1.2469 train_time:300625ms step_avg:100.21ms
+step:3100/20000 train_loss:2.0536 train_time:310605ms step_avg:100.20ms
+step:3200/20000 train_loss:2.1821 train_time:320597ms step_avg:100.19ms
+step:3300/20000 train_loss:2.0807 train_time:330522ms step_avg:100.16ms
+step:3400/20000 train_loss:2.0249 train_time:340504ms step_avg:100.15ms
+step:3500/20000 train_loss:2.1864 train_time:350488ms step_avg:100.14ms
+step:3500/20000 val_loss:2.0867 val_bpb:1.2358 train_time:350515ms step_avg:100.15ms
+step:3600/20000 train_loss:2.0971 train_time:360481ms step_avg:100.13ms
+step:3700/20000 train_loss:2.0972 train_time:370467ms step_avg:100.13ms
+step:3800/20000 train_loss:2.0745 train_time:380402ms step_avg:100.11ms
+step:3900/20000 train_loss:2.0771 train_time:390409ms step_avg:100.10ms
+step:4000/20000 train_loss:1.9767 train_time:400381ms step_avg:100.10ms
+step:4000/20000 val_loss:2.0649 val_bpb:1.2230 train_time:400407ms step_avg:100.10ms
+step:4100/20000 train_loss:2.0111 train_time:410379ms step_avg:100.09ms
+step:4200/20000 train_loss:2.1512 train_time:420355ms step_avg:100.08ms
+step:4300/20000 train_loss:2.0497 train_time:430284ms step_avg:100.07ms
+step:4400/20000 train_loss:2.0278 train_time:440260ms step_avg:100.06ms
+step:4500/20000 train_loss:2.1155 train_time:450253ms step_avg:100.06ms
+step:4500/20000 val_loss:2.0376 val_bpb:1.2068 train_time:450280ms step_avg:100.06ms
+step:4600/20000 train_loss:1.8352 train_time:460233ms step_avg:100.05ms
+step:4700/20000 train_loss:2.2260 train_time:470153ms step_avg:100.03ms
+step:4800/20000 train_loss:2.4265 train_time:480133ms step_avg:100.03ms
+step:4900/20000 train_loss:2.0406 train_time:490112ms step_avg:100.02ms
+step:5000/20000 train_loss:2.0948 train_time:500100ms step_avg:100.02ms
+step:5000/20000 val_loss:2.0129 val_bpb:1.1922 train_time:500126ms step_avg:100.03ms
+step:5100/20000 train_loss:2.1123 train_time:510082ms step_avg:100.02ms
+step:5200/20000 train_loss:2.0305 train_time:520004ms step_avg:100.00ms
+step:5300/20000 train_loss:1.9923 train_time:529971ms step_avg:99.99ms
+swa:start step:5350
+step:5400/20000 train_loss:2.0323 train_time:540022ms step_avg:100.00ms
+step:5500/20000 train_loss:2.0009 train_time:550034ms step_avg:100.01ms
+step:5500/20000 val_loss:1.9842 val_bpb:1.1752 train_time:550089ms step_avg:100.02ms
+step:5600/20000 train_loss:1.9365 train_time:560089ms step_avg:100.02ms
+step:5700/20000 train_loss:1.9912 train_time:570062ms step_avg:100.01ms
+step:5800/20000 train_loss:1.9716 train_time:580099ms step_avg:100.02ms
+step:5900/20000 train_loss:1.8795 train_time:590129ms step_avg:100.02ms
+step:5999/20000 val_loss:1.9594 val_bpb:1.1605 train_time:600082ms step_avg:100.03ms
+stopping_early: wallclock_cap train_time:600082ms step:5999/20000
+peak memory allocated: 20841 MiB reserved: 21060 MiB
+swa:applying averaged 13 checkpoints
+Serialized model: 98437419 bytes
+Code size: 58616 bytes
+Total submission size: 98496035 bytes
+awq:calibrating alpha=0.5
+awq:scaled 61 layers
+Serialized model int6+zstd: 15394174 bytes
+Total submission size int8+zlib: 15452790 bytes
+awq:unscaled 61 layers after dequant
+final_eval_mode:sliding_window stride:64 batch_seqs:64
+final_int8_zlib_roundtrip val_loss:1.9420 val_bpb:1.1502 eval_time:180059ms
+final_int8_zlib_roundtrip_exact val_loss:1.94198177 val_bpb:1.15015403
diff --git a/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed43.txt b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed43.txt
new file mode 100644
index 000000000..b52ee75a7
--- /dev/null
+++ b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed43.txt
@@ -0,0 +1,1522 @@
+"""
+The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder.
+
+Hard stop: To keep readable for newcomers, let's make sure `train_gpt.py` and `train_gpt_mlx.py` never are longer than 1500 lines.
+"""
+
+from __future__ import annotations
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import time
+import uuid
+import zlib
+from pathlib import Path
+
+try:
+    import zstandard
+    _COMPRESSOR = "zstd"
+except ImportError:
+    _COMPRESSOR = "zlib"
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+# -----------------------------
+# HYPERPARAMETERS
+# -----------------------------
+
+class Hyperparameters:
+    data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024")
+    train_files = os.path.join(data_path, "fineweb_train_*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_*.bin")
+    tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model")
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+    seed = int(os.environ.get("SEED", 42))
+
+    val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288))
+    val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500))
+    train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100))
+
+    iterations = int(os.environ.get("ITERATIONS", 20000))
+    warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000))
+    warmup_steps = int(os.environ.get("WARMUP_STEPS", 20))
+    train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432))
+    train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048))
+    max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0))
+    qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5))
+
+    vocab_size = int(os.environ.get("VOCAB_SIZE", 1024))
+    num_layers = int(os.environ.get("NUM_LAYERS", 11))
+    unique_layers = int(os.environ.get("UNIQUE_LAYERS", 10))
+    num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4))
+    model_dim = int(os.environ.get("MODEL_DIM", 512))
+    num_heads = int(os.environ.get("NUM_HEADS", 8))
+    mlp_mult = float(os.environ.get("MLP_MULT", 3.0))
+    mlp_activation = os.environ.get("MLP_ACTIVATION", "relu_sq")
+    tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1")))
+    rope_base = float(os.environ.get("ROPE_BASE", 10000.0))
+    logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0))
+
+    embed_lr = float(os.environ.get("EMBED_LR", 0.6))
+    head_lr = float(os.environ.get("HEAD_LR", 0.008))
+    tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03))
+    tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005))
+    matrix_lr = float(os.environ.get("MATRIX_LR", 0.02))
+    scalar_lr = float(os.environ.get("SCALAR_LR", 0.02))
+    muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99))
+    muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5))
+    muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500))
+    momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1")))
+    momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85))
+    momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95))
+    momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50))
+    beta1 = float(os.environ.get("BETA1", 0.9))
+    beta2 = float(os.environ.get("BETA2", 0.95))
+    adam_eps = float(os.environ.get("ADAM_EPS", 1e-8))
+    grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3))
+    weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04))
+
+    eval_stride = int(os.environ.get("EVAL_STRIDE", 64))
+    eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32))
+
+    bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240))
+    bigram_dim = int(os.environ.get("BIGRAM_DIM", 128))
+
+    swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1")))
+    swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4))
+    swa_every = int(os.environ.get("SWA_EVERY", 50))
+
+# -----------------------------
+# MUON OPTIMIZER
+# -----------------------------
+
+def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, weight_decay: float = 0.0):
+        super().__init__(
+            params,
+            dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, weight_decay=weight_decay),
+        )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr = group["lr"]
+            momentum = group["momentum"]
+            backend_steps = group["backend_steps"]
+            nesterov = group["nesterov"]
+
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr : curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+
+            wd = group.get("weight_decay", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.data.mul_(1.0 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+
+# -----------------------------
+# TOKENIZER-AGNOSTIC EVALUATION
+# -----------------------------
+
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}")
+    return tokens[: usable + 1]
+
+
+def eval_val(
+    args: Hyperparameters,
+    model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    grad_accum_steps: int,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+) -> tuple[float, float]:
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    if local_batch_tokens < args.train_seq_len:
+        raise ValueError(
+            "VAL_BATCH_SIZE must provide at least one sequence per rank; "
+            f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, "
+            f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}"
+        )
+    local_batch_seqs = local_batch_tokens // args.train_seq_len
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * args.train_seq_len
+            raw_end = batch_seq_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, args.train_seq_len)
+            y = local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+    val_loss = val_loss_sum / val_token_count
+    bits_per_token = val_loss.item() / math.log(2.0)
+    tokens_per_byte = val_token_count.item() / val_byte_count.item()
+    model.train()
+    return float(val_loss.item()), float(bits_per_token * tokens_per_byte)
+
+
+# -----------------------------
+# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed)
+# -----------------------------
+
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "CONTROL_TENSOR_NAME_PATTERNS",
+        "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,bigram.scale",
+    ).split(",")
+    if pattern
+)
+FP16_KEEP_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS",
+        ",".join(CONTROL_TENSOR_NAME_PATTERNS),
+    ).split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_MAX_NUMEL = 65_536
+INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_PERCENTILE = 99.99984
+INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0
+
+def tensor_nbytes(t: Tensor) -> int:
+    return int(t.numel()) * int(t.element_size())
+
+def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        clip_abs = (
+            torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1)
+            if t32.numel()
+            else torch.empty((t32.shape[0],), dtype=torch.float32)
+        )
+        clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None])
+        scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0)
+        q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous()
+        return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+    clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous()
+    return q, scale
+
+
+def _classify_param(name: str) -> str:
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if "bigram" in name:
+        return "bigram"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+
+def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        row_max = t32.abs().amax(dim=1)
+        scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16)
+        scale = scale.clamp_min(torch.finfo(torch.float16).tiny)
+        q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8)
+        return q, scale
+    amax = t32.abs().max().item()
+    scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16)
+    q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8)
+    return q, scale
+
+def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]):
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = _classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 8192:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS):
+            result[name] = t.to(dtype=torch.float16).contiguous()
+            meta[name] = "passthrough_fp16"
+            continue
+        if cat in int6_cats and t.ndim >= 1:
+            clip = 15 if cat == "mlp" else 31  # int5 for MLP, int6 for attention
+            q, s = quantize_intN_per_row(t, clip_range=clip)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    return result, meta
+
+def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object],
+                          template_sd: dict[str, Tensor]) -> dict[str, Tensor]:
+    out: dict[str, Tensor] = {}
+    for name, orig in template_sd.items():
+        info = meta[name]
+        orig_dtype = orig.dtype
+        if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"):
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + ".q"], result[name + ".scale"]
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+
+
+# -----------------------------
+# DATA LOADING
+# -----------------------------
+
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    expected_size = header_bytes + num_tokens * token_bytes
+    if file.stat().st_size != expected_size:
+        raise ValueError(f"Shard size mismatch for {file}: expected {expected_size} bytes")
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    if tokens_np.size != num_tokens:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = load_data_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self) -> None:
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = load_data_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks: list[Tensor] = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos : self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start : start + per_rank_span].to(dtype=torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+
+# -----------------------------
+# TRANSFORMER MODULES
+# -----------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+
+class CastedLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, None, :, :]
+            self._sin_cached = freqs.sin()[None, None, :, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        if dim % num_heads != 0:
+            raise ValueError("model_dim must be divisible by num_heads")
+        if num_heads % num_kv_heads != 0:
+            raise ValueError("num_heads must be divisible by num_kv_heads")
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        if self.head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin)
+        k = apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None]
+        y = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=None, is_causal=True,
+            enable_gqa=(self.num_kv_heads != self.num_heads),
+        )
+        y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, mlp_mult: float):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = torch.relu(self.fc(x))
+        return self.proj(x.square())
+
+
+class SmearGate(nn.Module):
+    """Blend each token's embedding with the previous token's embedding."""
+    def __init__(self, dim: int):
+        super().__init__()
+        self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :]
+        x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1)
+        return (1 - g) * x + g * x_prev
+
+
+class BigramHashEmbedding(nn.Module):
+    """Hash consecutive token pairs into a learned embedding table."""
+    def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int):
+        super().__init__()
+        self.bigram_vocab_size = bigram_vocab_size
+        self.embed = nn.Embedding(bigram_vocab_size, bigram_dim)
+        nn.init.zeros_(self.embed.weight)
+        self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32))
+
+    def bigram_hash(self, tokens: Tensor) -> Tensor:
+        t = tokens.to(torch.int32)
+        mod = self.bigram_vocab_size - 1
+        out = torch.empty_like(t)
+        out[..., 0] = mod
+        out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod
+        return out.long()
+
+    def forward(self, token_ids: Tensor) -> Tensor:
+        h = self.embed(self.bigram_hash(token_ids))
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x))
+        x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out
+        x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        num_layers: int,
+        model_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        mlp_mult: float,
+        tie_embeddings: bool,
+        tied_embed_init_std: float,
+        logit_softcap: float,
+        rope_base: float,
+        qk_gain_init: float,
+        bigram_vocab_size: int = 0,
+        bigram_dim: int = 128,
+        unique_layers: int = 0,
+    ):
+        super().__init__()
+        if logit_softcap <= 0.0:
+            raise ValueError(f"logit_softcap must be positive, got {logit_softcap}")
+        self.tie_embeddings = tie_embeddings
+        self.tied_embed_init_std = tied_embed_init_std
+        self.logit_softcap = logit_softcap
+        self.tok_emb = nn.Embedding(vocab_size, model_dim)
+        self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+        self.smear = SmearGate(model_dim)
+        # Layer sharing: create unique_layers physical blocks, map num_layers virtual layers to them
+        n_unique = unique_layers if unique_layers > 0 else num_layers
+        self.blocks = nn.ModuleList(
+            [
+                Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init)
+                for _ in range(n_unique)
+            ]
+        )
+        # Mapping from virtual layer index → physical block index
+        # Last virtual layer shares with the last physical block
+        self._layer_map = list(range(min(n_unique, num_layers)))
+        while len(self._layer_map) < num_layers:
+            self._layer_map.append(n_unique - 1)
+        self.final_norm = RMSNorm()
+        self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        num_layers = len(self._layer_map)  # virtual depth, not physical blocks
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, "_zero_init", False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+                    if ".proj." in name or name.endswith(".proj"):
+                        with torch.no_grad():
+                            module.weight.mul_(1.0 / math.sqrt(2 * num_layers))
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x).reshape(-1, x.size(-1))
+        targets = target_ids.reshape(-1)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            if self.lm_head is None:
+                raise RuntimeError("lm_head is required when tie_embeddings=False")
+            logits_proj = self.lm_head(x)
+        logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+        return F.cross_entropy(logits.float(), targets, reduction="mean")
+
+    def forward_logits(self, input_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+
+
+def eval_val_sliding(
+    args: Hyperparameters,
+    base_model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+    stride: int,
+    batch_seqs: int = 32,
+) -> tuple[float, float]:
+    seq_len = args.train_seq_len
+    total_tokens = val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, stride)
+                     if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0]
+    total_windows = len(window_starts)
+    my_s = (total_windows * rank) // world_size
+    my_e = (total_windows * (rank + 1)) // world_size
+    my_windows = window_starts[my_s:my_e]
+
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    base_model.eval()
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens: list[int] = []
+            for i, ws in enumerate(batch_ws):
+                end = min(ws + seq_len, total_tokens)
+                wlen = end - ws
+                wlens.append(wlen)
+                chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits = base_model.forward_logits(x_batch)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else max(wlen - stride, 0)
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = base_bytes_lut[tgt].to(torch.float64)
+                tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+            if rank == 0 and (bi // batch_seqs) % 50 == 0:
+                done = min(bi + batch_seqs, len(my_windows))
+                pct = done / len(my_windows) * 100
+                running_bpb = 0.0
+                if token_count.item() > 0:
+                    rl = (loss_sum / token_count).item()
+                    running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item())
+                print(f"  sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True)
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    val_loss = (loss_sum / token_count).item()
+    bits_per_token = val_loss / math.log(2.0)
+    tokens_per_byte = token_count.item() / byte_count.item()
+    base_model.train()
+    return val_loss, bits_per_token * tokens_per_byte
+
+
+# -----------------------------
+# TRAINING
+# -----------------------------
+
+def main() -> None:
+    global zeropower_via_newtonschulz5
+
+    code = Path(__file__).read_text(encoding="utf-8")
+    args = Hyperparameters()
+    zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral")
+    grad_accum_steps = 8 // world_size
+    grad_scale = 1.0 / grad_accum_steps
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+
+    logfile = None
+    if master_process:
+        os.makedirs("logs", exist_ok=True)
+        logfile = f"logs/{args.run_id}.txt"
+        print(logfile)
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile is not None:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+    log0(f"Running Python {sys.version}", console=False)
+    log0(f"Running PyTorch {torch.__version__}", console=False)
+    log0(
+        subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout,
+        console=False,
+    )
+    log0("=" * 100, console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    if not args.tokenizer_path.endswith(".model"):
+        raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}")
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    if int(sp.vocab_size()) != args.vocab_size:
+        raise ValueError(
+            f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}"
+        )
+    dataset_dir = Path(args.data_path).resolve()
+    actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin")))
+    val_tokens = load_validation_tokens(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(
+        sp, args.vocab_size, device
+    )
+    log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}")
+    log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}")
+    log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}")
+
+    # MODEL + OPTIMIZER SETUP
+    base_model = GPT(
+        vocab_size=args.vocab_size,
+        num_layers=args.num_layers,
+        model_dim=args.model_dim,
+        num_heads=args.num_heads,
+        num_kv_heads=args.num_kv_heads,
+        mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings,
+        tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap,
+        rope_base=args.rope_base,
+        qk_gain_init=args.qk_gain_init,
+        bigram_vocab_size=args.bigram_vocab_size,
+        bigram_dim=args.bigram_dim,
+        unique_layers=args.unique_layers,
+    ).to(device).bfloat16()
+    for module in base_model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model
+
+    block_named_params = list(base_model.blocks.named_parameters())
+    matrix_params = [
+        p for name, p in block_named_params
+        if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    scalar_params = [
+        p for name, p in block_named_params
+        if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    if base_model.skip_weights.numel() > 0:
+        scalar_params.append(base_model.skip_weights)
+    scalar_params.append(base_model.smear.gate)
+    if base_model.bigram is not None:
+        scalar_params.append(base_model.bigram.scale)
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}]
+    if base_model.bigram is not None:
+        tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr})
+        if base_model.bigram.proj is not None:
+            matrix_params.append(base_model.bigram.proj.weight)
+
+    optimizer_tok = torch.optim.AdamW(
+        tok_params,
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizer_muon = Muon(
+        matrix_params,
+        lr=args.matrix_lr,
+        momentum=args.muon_momentum,
+        backend_steps=args.muon_backend_steps,
+        weight_decay=0.04,
+    )
+    for group in optimizer_muon.param_groups:
+        group["base_lr"] = args.matrix_lr
+    optimizer_scalar = torch.optim.AdamW(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar]
+    if base_model.lm_head is not None:
+        optimizer_head = torch.optim.Adam(
+            [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}],
+            betas=(args.beta1, args.beta2),
+            eps=args.adam_eps,
+            fused=True,
+        )
+        optimizers.insert(1, optimizer_head)
+
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"model_params:{n_params}")
+    log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}")
+    log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}")
+    log0(
+        f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} "
+        f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}"
+    )
+    log0(
+        f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} "
+        f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} "
+        f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}"
+    )
+    log0(f"seed:{args.seed}")
+
+    # DATA LOADER & MODEL WARMUP
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all() -> None:
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float) -> float:
+        if args.warmdown_iters <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = max(args.iterations - args.warmdown_iters, 0)
+            return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0
+        step_ms = elapsed_ms / max(step, 1)
+        warmdown_ms = args.warmdown_iters * step_ms
+        remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+        return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+
+    if args.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(args.warmup_steps):
+            zero_grad_all()
+            for micro_step in range(grad_accum_steps):
+                if distributed:
+                    model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+                x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                    warmup_loss = model(x, y)
+                (warmup_loss * grad_scale).backward()
+            for opt in optimizers:
+                opt.step()
+            zero_grad_all()
+            if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps:
+                log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}")
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        zero_grad_all()
+        if distributed:
+            model.require_backward_grad_sync = True
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # MAIN TRAINING LOOP
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    swa_state: dict[str, Tensor] | None = None
+    swa_count = 0
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    step = 0
+    while True:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            )
+            log0(
+                f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms"
+            )
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(
+                    f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms "
+                    f"step:{step}/{args.iterations}"
+                )
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+        zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+        for micro_step in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+            x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        if step < args.muon_momentum_warmup_steps:
+            # Linear warmup phase
+            frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0
+            muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+        elif args.momentum_cyclic:
+            # Cyclic momentum: triangle wave between momentum_min and momentum_max
+            period = args.momentum_cycle_period * 2
+            pos = (step % period) / period
+            if pos < 0.5:
+                muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2)
+            else:
+                muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2)
+        else:
+            muon_momentum = args.muon_momentum
+        for group in optimizer_muon.param_groups:
+            group["momentum"] = muon_momentum
+
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group["lr"] = group["base_lr"] * scale
+
+        if args.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm)
+        for opt in optimizers:
+            opt.step()
+        zero_grad_all()
+
+        step += 1
+        approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+
+        # SWA: collect checkpoints during warmdown
+        if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0:
+            if swa_state is None:
+                swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}
+                swa_count = 1
+                log0(f"swa:start step:{step}")
+            else:
+                for name, t in base_model.state_dict().items():
+                    swa_state[name] += t.detach().cpu()
+                swa_count += 1
+
+        should_log_train = (
+            args.train_log_every > 0
+            and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None)
+        )
+        if should_log_train:
+            log0(
+                f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} "
+                f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms"
+            )
+
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log0(
+        f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+
+    # Apply SWA if collected
+    if args.swa_enabled and swa_state is not None and swa_count > 1:
+        log0(f"swa:applying averaged {swa_count} checkpoints")
+        current_state = base_model.state_dict()
+        avg_state = {
+            name: (tensor / swa_count).to(dtype=current_state[name].dtype)
+            for name, tensor in swa_state.items()
+        }
+        base_model.load_state_dict(avg_state, strict=True)
+
+    # SERIALIZATION + ROUNDTRIP VALIDATION
+    if master_process:
+        torch.save(base_model.state_dict(), "final_model.pt")
+        model_bytes = os.path.getsize("final_model.pt")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model: {model_bytes} bytes")
+        log0(f"Code size: {code_bytes} bytes")
+        log0(f"Total submission size: {model_bytes + code_bytes} bytes")
+
+    # Magnitude pruning: zero out smallest weights to improve compression
+    with torch.no_grad():
+        for name, param in base_model.named_parameters():
+            if param.ndim == 2 and param.numel() > 65536:
+                threshold = torch.quantile(param.abs().float().flatten(), 0.03)
+                mask = param.abs() < threshold
+                param.masked_fill_(mask, 0.0)
+
+    # AWQ: Activation-aware weight scaling before quantization
+    awq_alpha = float(os.environ.get("AWQ_ALPHA", 0.5))
+    awq_enabled = bool(int(os.environ.get("AWQ_ENABLED", "1")))
+    if awq_enabled:
+        log0(f"awq:calibrating alpha={awq_alpha}")
+        # Step 1: Collect per-channel activation magnitudes via hooks
+        act_stats: dict[str, Tensor] = {}
+        hooks = []
+        def make_hook(name):
+            def hook_fn(module, input, output):
+                x = input[0] if isinstance(input, tuple) else input
+                if x.ndim >= 2:
+                    # Mean absolute activation per channel (last dim)
+                    s = x.detach().float().abs().mean(dim=tuple(range(x.ndim - 1)))
+                    if name in act_stats:
+                        act_stats[name] = act_stats[name] + s
+                    else:
+                        act_stats[name] = s
+            return hook_fn
+
+        for name, module in base_model.named_modules():
+            if isinstance(module, (nn.Linear, CastedLinear)):
+                hooks.append(module.register_forward_hook(make_hook(name)))
+
+        # Step 2: Run calibration batches (use val data, 4 batches)
+        base_model.eval()
+        n_calib_batches = 4
+        calib_seq_len = args.train_seq_len
+        calib_batch_seqs = 16
+        calib_tokens_per_batch = calib_batch_seqs * calib_seq_len
+        with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            for cb in range(n_calib_batches):
+                start = cb * calib_tokens_per_batch
+                end = start + calib_tokens_per_batch + 1
+                if end > val_tokens.numel():
+                    break
+                local = val_tokens[start:end].to(device=device, dtype=torch.int64)
+                x = local[:-1].reshape(-1, calib_seq_len)
+                base_model.forward_logits(x)
+
+        for h in hooks:
+            h.remove()
+
+        # Normalize activation stats
+        for name in act_stats:
+            act_stats[name] = act_stats[name] / n_calib_batches
+
+        # Step 3: Scale weight columns by s^alpha
+        awq_scales = {}
+        with torch.no_grad():
+            for name, module in base_model.named_modules():
+                if isinstance(module, (nn.Linear, CastedLinear)) and name in act_stats:
+                    s = act_stats[name].to(module.weight.device)
+                    s = s.clamp_min(1e-6)
+                    scale = s.pow(awq_alpha)
+                    # Scale weight columns (input channels)
+                    if module.weight.shape[1] == scale.shape[0]:
+                        module.weight.data = module.weight.data * scale.unsqueeze(0)
+                        awq_scales[name] = scale.cpu()
+                        # Store inverse scale to apply to inputs at inference
+                        # We'll fold this into the state dict
+
+        log0(f"awq:scaled {len(awq_scales)} layers")
+
+    # INT6 mixed quantization + zstd/zlib export
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()}
+    # Store AWQ inverse scales in the state dict for inference compensation
+    if awq_enabled and awq_scales:
+        for lname, scale in awq_scales.items():
+            sd_cpu[f"_awq_inv_scale.{lname}"] = (1.0 / scale).to(torch.float16)
+    quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"})
+    quant_buf = io.BytesIO()
+    torch.save({"w": quant_result, "m": quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    if _COMPRESSOR == "zstd":
+        quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw)
+    else:
+        quant_blob = zlib.compress(quant_raw, 9)
+    if master_process:
+        with open("final_model.int8.ptz", "wb") as f:
+            f.write(quant_blob)
+        quant_file_bytes = os.path.getsize("final_model.int8.ptz")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes")
+        log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes")
+
+    if distributed:
+        dist.barrier()
+    with open("final_model.int8.ptz", "rb") as f:
+        quant_blob_disk = f.read()
+    if _COMPRESSOR == "zstd":
+        decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk)
+    else:
+        decompressed = zlib.decompress(quant_blob_disk)
+    quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu")
+    deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu)
+    # AWQ: undo column scaling after dequantization
+    if awq_enabled:
+        awq_inv_keys = [k for k in deq_state if k.startswith("_awq_inv_scale.")]
+        for inv_key in awq_inv_keys:
+            inv_scale = deq_state.pop(inv_key).float()
+            layer_name = inv_key.replace("_awq_inv_scale.", "")
+            weight_key = layer_name + ".weight"
+            if weight_key in deq_state:
+                # Undo: W_orig = W_scaled * inv_scale (per column)
+                deq_state[weight_key] = deq_state[weight_key].float() * inv_scale.unsqueeze(0)
+                deq_state[weight_key] = deq_state[weight_key].to(dtype=sd_cpu[weight_key].dtype)
+        log0(f"awq:unscaled {len(awq_inv_keys)} layers after dequant")
+    # Remove any remaining AWQ keys before loading
+    deq_state = {k: v for k, v in deq_state.items() if not k.startswith("_awq_")}
+    base_model.load_state_dict(deq_state, strict=True)
+
+    # Sliding window eval on int6-roundtripped weights
+    torch.cuda.synchronize()
+    t_qeval = time.perf_counter()
+    if args.eval_stride > 0 and args.eval_stride < args.train_seq_len:
+        log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}")
+        q_val_loss, q_val_bpb = eval_val_sliding(
+            args, base_model, rank, world_size, device,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            stride=args.eval_stride, batch_seqs=args.eval_batch_seqs,
+        )
+    else:
+        log0("final_eval_mode:standard")
+        q_val_loss, q_val_bpb = eval_val(
+            args, model, rank, world_size, device, grad_accum_steps,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+        )
+    torch.cuda.synchronize()
+    log0(
+        f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+        f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms"
+    )
+    log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
+# fixes applied
+# tuned
+
+====================================================================================================
+Running Python 3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0]
+Running PyTorch 2.9.1+cu128
+Tue Mar 24 11:39:20 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 580.126.09             Driver Version: 580.126.09     CUDA Version: 13.0     |
++-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 80GB HBM3          On  |   00000000:19:00.0 Off |                    0 |
+| N/A   47C    P0            129W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   1  NVIDIA H100 80GB HBM3          On  |   00000000:3B:00.0 Off |                    0 |
+| N/A   36C    P0            118W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   2  NVIDIA H100 80GB HBM3          On  |   00000000:4C:00.0 Off |                    0 |
+| N/A   34C    P0            118W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   3  NVIDIA H100 80GB HBM3          On  |   00000000:5D:00.0 Off |                    0 |
+| N/A   47C    P0            134W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   4  NVIDIA H100 80GB HBM3          On  |   00000000:9B:00.0 Off |                    0 |
+| N/A   48C    P0            131W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   5  NVIDIA H100 80GB HBM3          On  |   00000000:BB:00.0 Off |                    0 |
+| N/A   38C    P0            128W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   6  NVIDIA H100 80GB HBM3          On  |   00000000:CB:00.0 Off |                    0 |
+| N/A   46C    P0            127W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   7  NVIDIA H100 80GB HBM3          On  |   00000000:DB:00.0 Off |                    0 |
+| N/A   36C    P0            119W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|  No running processes found                                                             |
++-----------------------------------------------------------------------------------------+
+
+====================================================================================================
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:80
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+model_params:25517137
+world_size:8 grad_accum_steps:1
+attention_mode:gqa num_heads:8 num_kv_heads:4
+tie_embeddings:True embed_lr:0.035 matrix_lr:0.025 scalar_lr:0.025
+train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:43
+warmup_step:1/20
+warmup_step:2/20
+warmup_step:3/20
+warmup_step:4/20
+warmup_step:5/20
+warmup_step:6/20
+warmup_step:7/20
+warmup_step:8/20
+warmup_step:9/20
+warmup_step:10/20
+warmup_step:11/20
+warmup_step:12/20
+warmup_step:13/20
+warmup_step:14/20
+warmup_step:15/20
+warmup_step:16/20
+warmup_step:17/20
+warmup_step:18/20
+warmup_step:19/20
+warmup_step:20/20
+step:0/20000 val_loss:6.9285 val_bpb:4.1035 train_time:0ms step_avg:0.02ms
+step:1/20000 train_loss:6.9296 train_time:149ms step_avg:148.74ms
+step:2/20000 train_loss:8.4791 train_time:221ms step_avg:110.74ms
+step:3/20000 train_loss:7.8364 train_time:318ms step_avg:105.92ms
+step:4/20000 train_loss:7.2856 train_time:415ms step_avg:103.85ms
+step:5/20000 train_loss:7.0369 train_time:514ms step_avg:102.84ms
+step:6/20000 train_loss:6.8438 train_time:612ms step_avg:102.03ms
+step:7/20000 train_loss:6.7871 train_time:711ms step_avg:101.54ms
+step:8/20000 train_loss:6.8742 train_time:810ms step_avg:101.19ms
+step:9/20000 train_loss:6.5695 train_time:908ms step_avg:100.86ms
+step:10/20000 train_loss:6.2076 train_time:1005ms step_avg:100.52ms
+step:100/20000 train_loss:3.1589 train_time:10032ms step_avg:100.32ms
+step:200/20000 train_loss:2.3736 train_time:20030ms step_avg:100.15ms
+step:300/20000 train_loss:2.5403 train_time:30058ms step_avg:100.19ms
+step:400/20000 train_loss:2.4110 train_time:40100ms step_avg:100.25ms
+step:500/20000 train_loss:2.3962 train_time:50106ms step_avg:100.21ms
+step:500/20000 val_loss:2.3542 val_bpb:1.3943 train_time:50133ms step_avg:100.27ms
+step:600/20000 train_loss:2.3361 train_time:60176ms step_avg:100.29ms
+step:700/20000 train_loss:2.3427 train_time:70239ms step_avg:100.34ms
+step:800/20000 train_loss:2.2381 train_time:80296ms step_avg:100.37ms
+step:900/20000 train_loss:2.1292 train_time:90346ms step_avg:100.38ms
+step:1000/20000 train_loss:2.2797 train_time:100338ms step_avg:100.34ms
+step:1000/20000 val_loss:2.2304 val_bpb:1.3210 train_time:100365ms step_avg:100.36ms
+step:1100/20000 train_loss:2.3257 train_time:110393ms step_avg:100.36ms
+step:1200/20000 train_loss:2.3582 train_time:120427ms step_avg:100.36ms
+step:1300/20000 train_loss:2.1011 train_time:130471ms step_avg:100.36ms
+step:1400/20000 train_loss:2.1876 train_time:140507ms step_avg:100.36ms
+step:1500/20000 train_loss:2.2261 train_time:150479ms step_avg:100.32ms
+step:1500/20000 val_loss:2.1915 val_bpb:1.2979 train_time:150505ms step_avg:100.34ms
+step:1600/20000 train_loss:2.0294 train_time:160501ms step_avg:100.31ms
+step:1700/20000 train_loss:2.1036 train_time:170525ms step_avg:100.31ms
+step:1800/20000 train_loss:2.1260 train_time:180535ms step_avg:100.30ms
+step:1900/20000 train_loss:2.0983 train_time:190498ms step_avg:100.26ms
+step:2000/20000 train_loss:2.0471 train_time:200512ms step_avg:100.26ms
+step:2000/20000 val_loss:2.1136 val_bpb:1.2518 train_time:200539ms step_avg:100.27ms
+step:2100/20000 train_loss:2.0326 train_time:210518ms step_avg:100.25ms
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+step:2500/20000 train_loss:2.1760 train_time:250447ms step_avg:100.18ms
+step:2500/20000 val_loss:2.1204 val_bpb:1.2558 train_time:250473ms step_avg:100.19ms
+step:2600/20000 train_loss:2.1216 train_time:260431ms step_avg:100.17ms
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+step:2900/20000 train_loss:2.0422 train_time:290367ms step_avg:100.13ms
+step:3000/20000 train_loss:2.1734 train_time:300362ms step_avg:100.12ms
+step:3000/20000 val_loss:2.1066 val_bpb:1.2476 train_time:300388ms step_avg:100.13ms
+step:3100/20000 train_loss:2.0504 train_time:310354ms step_avg:100.11ms
+step:3200/20000 train_loss:2.1826 train_time:320337ms step_avg:100.11ms
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+step:3500/20000 train_loss:2.1849 train_time:350219ms step_avg:100.06ms
+step:3500/20000 val_loss:2.0866 val_bpb:1.2358 train_time:350244ms step_avg:100.07ms
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+step:4000/20000 train_loss:1.9785 train_time:400040ms step_avg:100.01ms
+step:4000/20000 val_loss:2.0639 val_bpb:1.2224 train_time:400067ms step_avg:100.02ms
+step:4100/20000 train_loss:2.0115 train_time:410015ms step_avg:100.00ms
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+step:4500/20000 train_loss:2.1164 train_time:449828ms step_avg:99.96ms
+step:4500/20000 val_loss:2.0388 val_bpb:1.2075 train_time:449853ms step_avg:99.97ms
+step:4600/20000 train_loss:1.8345 train_time:459799ms step_avg:99.96ms
+step:4700/20000 train_loss:2.2178 train_time:469706ms step_avg:99.94ms
+step:4800/20000 train_loss:2.4194 train_time:479665ms step_avg:99.93ms
+step:4900/20000 train_loss:2.0402 train_time:489639ms step_avg:99.93ms
+step:5000/20000 train_loss:2.0891 train_time:499610ms step_avg:99.92ms
+step:5000/20000 val_loss:2.0117 val_bpb:1.1915 train_time:499637ms step_avg:99.93ms
+step:5100/20000 train_loss:2.1118 train_time:509574ms step_avg:99.92ms
+step:5200/20000 train_loss:2.0281 train_time:519474ms step_avg:99.90ms
+step:5300/20000 train_loss:1.9924 train_time:529438ms step_avg:99.89ms
+swa:start step:5350
+step:5400/20000 train_loss:2.0311 train_time:539475ms step_avg:99.90ms
+step:5500/20000 train_loss:2.0004 train_time:549484ms step_avg:99.91ms
+step:5500/20000 val_loss:1.9834 val_bpb:1.1747 train_time:549536ms step_avg:99.92ms
+step:5600/20000 train_loss:1.9374 train_time:559498ms step_avg:99.91ms
+step:5700/20000 train_loss:1.9898 train_time:569462ms step_avg:99.91ms
+step:5800/20000 train_loss:1.9719 train_time:579486ms step_avg:99.91ms
+step:5900/20000 train_loss:1.8830 train_time:589542ms step_avg:99.92ms
+step:6000/20000 train_loss:1.9237 train_time:599555ms step_avg:99.93ms
+step:6000/20000 val_loss:1.9583 val_bpb:1.1598 train_time:599622ms step_avg:99.94ms
+step:6005/20000 val_loss:1.9583 val_bpb:1.1598 train_time:600115ms step_avg:99.94ms
+stopping_early: wallclock_cap train_time:600115ms step:6005/20000
+peak memory allocated: 20841 MiB reserved: 21060 MiB
+swa:applying averaged 14 checkpoints
+Serialized model: 98437419 bytes
+Code size: 58616 bytes
+Total submission size: 98496035 bytes
+awq:calibrating alpha=0.5
+awq:scaled 61 layers
+Serialized model int6+zstd: 15458563 bytes
+Total submission size int8+zlib: 15517179 bytes
+awq:unscaled 61 layers after dequant
+final_eval_mode:sliding_window stride:64 batch_seqs:64
+final_int8_zlib_roundtrip val_loss:1.9408 val_bpb:1.1494 eval_time:180159ms
+final_int8_zlib_roundtrip_exact val_loss:1.94077623 val_bpb:1.14944004
diff --git a/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed44.txt b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed44.txt
new file mode 100644
index 000000000..69e4a3c2e
--- /dev/null
+++ b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/logs/submission_exp18_awq-cyclic-relusq-11Lshared_8xH100_seed44.txt
@@ -0,0 +1,1521 @@
+"""
+The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder.
+
+Hard stop: To keep readable for newcomers, let's make sure `train_gpt.py` and `train_gpt_mlx.py` never are longer than 1500 lines.
+"""
+
+from __future__ import annotations
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import time
+import uuid
+import zlib
+from pathlib import Path
+
+try:
+    import zstandard
+    _COMPRESSOR = "zstd"
+except ImportError:
+    _COMPRESSOR = "zlib"
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+# -----------------------------
+# HYPERPARAMETERS
+# -----------------------------
+
+class Hyperparameters:
+    data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024")
+    train_files = os.path.join(data_path, "fineweb_train_*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_*.bin")
+    tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model")
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+    seed = int(os.environ.get("SEED", 42))
+
+    val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288))
+    val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500))
+    train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100))
+
+    iterations = int(os.environ.get("ITERATIONS", 20000))
+    warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000))
+    warmup_steps = int(os.environ.get("WARMUP_STEPS", 20))
+    train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432))
+    train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048))
+    max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0))
+    qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5))
+
+    vocab_size = int(os.environ.get("VOCAB_SIZE", 1024))
+    num_layers = int(os.environ.get("NUM_LAYERS", 11))
+    unique_layers = int(os.environ.get("UNIQUE_LAYERS", 10))
+    num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4))
+    model_dim = int(os.environ.get("MODEL_DIM", 512))
+    num_heads = int(os.environ.get("NUM_HEADS", 8))
+    mlp_mult = float(os.environ.get("MLP_MULT", 3.0))
+    mlp_activation = os.environ.get("MLP_ACTIVATION", "relu_sq")
+    tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1")))
+    rope_base = float(os.environ.get("ROPE_BASE", 10000.0))
+    logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0))
+
+    embed_lr = float(os.environ.get("EMBED_LR", 0.6))
+    head_lr = float(os.environ.get("HEAD_LR", 0.008))
+    tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03))
+    tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005))
+    matrix_lr = float(os.environ.get("MATRIX_LR", 0.02))
+    scalar_lr = float(os.environ.get("SCALAR_LR", 0.02))
+    muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99))
+    muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5))
+    muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500))
+    momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1")))
+    momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85))
+    momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95))
+    momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50))
+    beta1 = float(os.environ.get("BETA1", 0.9))
+    beta2 = float(os.environ.get("BETA2", 0.95))
+    adam_eps = float(os.environ.get("ADAM_EPS", 1e-8))
+    grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3))
+    weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04))
+
+    eval_stride = int(os.environ.get("EVAL_STRIDE", 64))
+    eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32))
+
+    bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240))
+    bigram_dim = int(os.environ.get("BIGRAM_DIM", 128))
+
+    swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1")))
+    swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4))
+    swa_every = int(os.environ.get("SWA_EVERY", 50))
+
+# -----------------------------
+# MUON OPTIMIZER
+# -----------------------------
+
+def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, weight_decay: float = 0.0):
+        super().__init__(
+            params,
+            dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, weight_decay=weight_decay),
+        )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr = group["lr"]
+            momentum = group["momentum"]
+            backend_steps = group["backend_steps"]
+            nesterov = group["nesterov"]
+
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr : curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+
+            wd = group.get("weight_decay", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.data.mul_(1.0 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+
+# -----------------------------
+# TOKENIZER-AGNOSTIC EVALUATION
+# -----------------------------
+
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}")
+    return tokens[: usable + 1]
+
+
+def eval_val(
+    args: Hyperparameters,
+    model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    grad_accum_steps: int,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+) -> tuple[float, float]:
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    if local_batch_tokens < args.train_seq_len:
+        raise ValueError(
+            "VAL_BATCH_SIZE must provide at least one sequence per rank; "
+            f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, "
+            f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}"
+        )
+    local_batch_seqs = local_batch_tokens // args.train_seq_len
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * args.train_seq_len
+            raw_end = batch_seq_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, args.train_seq_len)
+            y = local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+    val_loss = val_loss_sum / val_token_count
+    bits_per_token = val_loss.item() / math.log(2.0)
+    tokens_per_byte = val_token_count.item() / val_byte_count.item()
+    model.train()
+    return float(val_loss.item()), float(bits_per_token * tokens_per_byte)
+
+
+# -----------------------------
+# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed)
+# -----------------------------
+
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "CONTROL_TENSOR_NAME_PATTERNS",
+        "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,bigram.scale",
+    ).split(",")
+    if pattern
+)
+FP16_KEEP_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS",
+        ",".join(CONTROL_TENSOR_NAME_PATTERNS),
+    ).split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_MAX_NUMEL = 65_536
+INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_PERCENTILE = 99.99984
+INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0
+
+def tensor_nbytes(t: Tensor) -> int:
+    return int(t.numel()) * int(t.element_size())
+
+def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        clip_abs = (
+            torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1)
+            if t32.numel()
+            else torch.empty((t32.shape[0],), dtype=torch.float32)
+        )
+        clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None])
+        scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0)
+        q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous()
+        return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+    clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous()
+    return q, scale
+
+
+def _classify_param(name: str) -> str:
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if "bigram" in name:
+        return "bigram"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+
+def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        row_max = t32.abs().amax(dim=1)
+        scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16)
+        scale = scale.clamp_min(torch.finfo(torch.float16).tiny)
+        q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8)
+        return q, scale
+    amax = t32.abs().max().item()
+    scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16)
+    q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8)
+    return q, scale
+
+def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]):
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = _classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 8192:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS):
+            result[name] = t.to(dtype=torch.float16).contiguous()
+            meta[name] = "passthrough_fp16"
+            continue
+        if cat in int6_cats and t.ndim >= 1:
+            clip = 15 if cat == "mlp" else 31  # int5 for MLP, int6 for attention
+            q, s = quantize_intN_per_row(t, clip_range=clip)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    return result, meta
+
+def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object],
+                          template_sd: dict[str, Tensor]) -> dict[str, Tensor]:
+    out: dict[str, Tensor] = {}
+    for name, orig in template_sd.items():
+        info = meta[name]
+        orig_dtype = orig.dtype
+        if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"):
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + ".q"], result[name + ".scale"]
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+
+
+# -----------------------------
+# DATA LOADING
+# -----------------------------
+
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    expected_size = header_bytes + num_tokens * token_bytes
+    if file.stat().st_size != expected_size:
+        raise ValueError(f"Shard size mismatch for {file}: expected {expected_size} bytes")
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    if tokens_np.size != num_tokens:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = load_data_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self) -> None:
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = load_data_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks: list[Tensor] = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos : self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start : start + per_rank_span].to(dtype=torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+
+# -----------------------------
+# TRANSFORMER MODULES
+# -----------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+
+class CastedLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, None, :, :]
+            self._sin_cached = freqs.sin()[None, None, :, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        if dim % num_heads != 0:
+            raise ValueError("model_dim must be divisible by num_heads")
+        if num_heads % num_kv_heads != 0:
+            raise ValueError("num_heads must be divisible by num_kv_heads")
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        if self.head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin)
+        k = apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None]
+        y = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=None, is_causal=True,
+            enable_gqa=(self.num_kv_heads != self.num_heads),
+        )
+        y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, mlp_mult: float):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = torch.relu(self.fc(x))
+        return self.proj(x.square())
+
+
+class SmearGate(nn.Module):
+    """Blend each token's embedding with the previous token's embedding."""
+    def __init__(self, dim: int):
+        super().__init__()
+        self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :]
+        x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1)
+        return (1 - g) * x + g * x_prev
+
+
+class BigramHashEmbedding(nn.Module):
+    """Hash consecutive token pairs into a learned embedding table."""
+    def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int):
+        super().__init__()
+        self.bigram_vocab_size = bigram_vocab_size
+        self.embed = nn.Embedding(bigram_vocab_size, bigram_dim)
+        nn.init.zeros_(self.embed.weight)
+        self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32))
+
+    def bigram_hash(self, tokens: Tensor) -> Tensor:
+        t = tokens.to(torch.int32)
+        mod = self.bigram_vocab_size - 1
+        out = torch.empty_like(t)
+        out[..., 0] = mod
+        out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod
+        return out.long()
+
+    def forward(self, token_ids: Tensor) -> Tensor:
+        h = self.embed(self.bigram_hash(token_ids))
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x))
+        x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out
+        x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        num_layers: int,
+        model_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        mlp_mult: float,
+        tie_embeddings: bool,
+        tied_embed_init_std: float,
+        logit_softcap: float,
+        rope_base: float,
+        qk_gain_init: float,
+        bigram_vocab_size: int = 0,
+        bigram_dim: int = 128,
+        unique_layers: int = 0,
+    ):
+        super().__init__()
+        if logit_softcap <= 0.0:
+            raise ValueError(f"logit_softcap must be positive, got {logit_softcap}")
+        self.tie_embeddings = tie_embeddings
+        self.tied_embed_init_std = tied_embed_init_std
+        self.logit_softcap = logit_softcap
+        self.tok_emb = nn.Embedding(vocab_size, model_dim)
+        self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+        self.smear = SmearGate(model_dim)
+        # Layer sharing: create unique_layers physical blocks, map num_layers virtual layers to them
+        n_unique = unique_layers if unique_layers > 0 else num_layers
+        self.blocks = nn.ModuleList(
+            [
+                Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init)
+                for _ in range(n_unique)
+            ]
+        )
+        # Mapping from virtual layer index → physical block index
+        # Last virtual layer shares with the last physical block
+        self._layer_map = list(range(min(n_unique, num_layers)))
+        while len(self._layer_map) < num_layers:
+            self._layer_map.append(n_unique - 1)
+        self.final_norm = RMSNorm()
+        self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        num_layers = len(self._layer_map)  # virtual depth, not physical blocks
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, "_zero_init", False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+                    if ".proj." in name or name.endswith(".proj"):
+                        with torch.no_grad():
+                            module.weight.mul_(1.0 / math.sqrt(2 * num_layers))
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x).reshape(-1, x.size(-1))
+        targets = target_ids.reshape(-1)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            if self.lm_head is None:
+                raise RuntimeError("lm_head is required when tie_embeddings=False")
+            logits_proj = self.lm_head(x)
+        logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+        return F.cross_entropy(logits.float(), targets, reduction="mean")
+
+    def forward_logits(self, input_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+
+
+def eval_val_sliding(
+    args: Hyperparameters,
+    base_model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+    stride: int,
+    batch_seqs: int = 32,
+) -> tuple[float, float]:
+    seq_len = args.train_seq_len
+    total_tokens = val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, stride)
+                     if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0]
+    total_windows = len(window_starts)
+    my_s = (total_windows * rank) // world_size
+    my_e = (total_windows * (rank + 1)) // world_size
+    my_windows = window_starts[my_s:my_e]
+
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    base_model.eval()
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens: list[int] = []
+            for i, ws in enumerate(batch_ws):
+                end = min(ws + seq_len, total_tokens)
+                wlen = end - ws
+                wlens.append(wlen)
+                chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits = base_model.forward_logits(x_batch)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else max(wlen - stride, 0)
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = base_bytes_lut[tgt].to(torch.float64)
+                tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+            if rank == 0 and (bi // batch_seqs) % 50 == 0:
+                done = min(bi + batch_seqs, len(my_windows))
+                pct = done / len(my_windows) * 100
+                running_bpb = 0.0
+                if token_count.item() > 0:
+                    rl = (loss_sum / token_count).item()
+                    running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item())
+                print(f"  sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True)
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    val_loss = (loss_sum / token_count).item()
+    bits_per_token = val_loss / math.log(2.0)
+    tokens_per_byte = token_count.item() / byte_count.item()
+    base_model.train()
+    return val_loss, bits_per_token * tokens_per_byte
+
+
+# -----------------------------
+# TRAINING
+# -----------------------------
+
+def main() -> None:
+    global zeropower_via_newtonschulz5
+
+    code = Path(__file__).read_text(encoding="utf-8")
+    args = Hyperparameters()
+    zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral")
+    grad_accum_steps = 8 // world_size
+    grad_scale = 1.0 / grad_accum_steps
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+
+    logfile = None
+    if master_process:
+        os.makedirs("logs", exist_ok=True)
+        logfile = f"logs/{args.run_id}.txt"
+        print(logfile)
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile is not None:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+    log0(f"Running Python {sys.version}", console=False)
+    log0(f"Running PyTorch {torch.__version__}", console=False)
+    log0(
+        subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout,
+        console=False,
+    )
+    log0("=" * 100, console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    if not args.tokenizer_path.endswith(".model"):
+        raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}")
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    if int(sp.vocab_size()) != args.vocab_size:
+        raise ValueError(
+            f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}"
+        )
+    dataset_dir = Path(args.data_path).resolve()
+    actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin")))
+    val_tokens = load_validation_tokens(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(
+        sp, args.vocab_size, device
+    )
+    log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}")
+    log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}")
+    log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}")
+
+    # MODEL + OPTIMIZER SETUP
+    base_model = GPT(
+        vocab_size=args.vocab_size,
+        num_layers=args.num_layers,
+        model_dim=args.model_dim,
+        num_heads=args.num_heads,
+        num_kv_heads=args.num_kv_heads,
+        mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings,
+        tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap,
+        rope_base=args.rope_base,
+        qk_gain_init=args.qk_gain_init,
+        bigram_vocab_size=args.bigram_vocab_size,
+        bigram_dim=args.bigram_dim,
+        unique_layers=args.unique_layers,
+    ).to(device).bfloat16()
+    for module in base_model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model
+
+    block_named_params = list(base_model.blocks.named_parameters())
+    matrix_params = [
+        p for name, p in block_named_params
+        if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    scalar_params = [
+        p for name, p in block_named_params
+        if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    if base_model.skip_weights.numel() > 0:
+        scalar_params.append(base_model.skip_weights)
+    scalar_params.append(base_model.smear.gate)
+    if base_model.bigram is not None:
+        scalar_params.append(base_model.bigram.scale)
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}]
+    if base_model.bigram is not None:
+        tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr})
+        if base_model.bigram.proj is not None:
+            matrix_params.append(base_model.bigram.proj.weight)
+
+    optimizer_tok = torch.optim.AdamW(
+        tok_params,
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizer_muon = Muon(
+        matrix_params,
+        lr=args.matrix_lr,
+        momentum=args.muon_momentum,
+        backend_steps=args.muon_backend_steps,
+        weight_decay=0.04,
+    )
+    for group in optimizer_muon.param_groups:
+        group["base_lr"] = args.matrix_lr
+    optimizer_scalar = torch.optim.AdamW(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar]
+    if base_model.lm_head is not None:
+        optimizer_head = torch.optim.Adam(
+            [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}],
+            betas=(args.beta1, args.beta2),
+            eps=args.adam_eps,
+            fused=True,
+        )
+        optimizers.insert(1, optimizer_head)
+
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"model_params:{n_params}")
+    log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}")
+    log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}")
+    log0(
+        f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} "
+        f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}"
+    )
+    log0(
+        f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} "
+        f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} "
+        f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}"
+    )
+    log0(f"seed:{args.seed}")
+
+    # DATA LOADER & MODEL WARMUP
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all() -> None:
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float) -> float:
+        if args.warmdown_iters <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = max(args.iterations - args.warmdown_iters, 0)
+            return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0
+        step_ms = elapsed_ms / max(step, 1)
+        warmdown_ms = args.warmdown_iters * step_ms
+        remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+        return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+
+    if args.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(args.warmup_steps):
+            zero_grad_all()
+            for micro_step in range(grad_accum_steps):
+                if distributed:
+                    model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+                x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                    warmup_loss = model(x, y)
+                (warmup_loss * grad_scale).backward()
+            for opt in optimizers:
+                opt.step()
+            zero_grad_all()
+            if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps:
+                log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}")
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        zero_grad_all()
+        if distributed:
+            model.require_backward_grad_sync = True
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # MAIN TRAINING LOOP
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    swa_state: dict[str, Tensor] | None = None
+    swa_count = 0
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    step = 0
+    while True:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            )
+            log0(
+                f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms"
+            )
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(
+                    f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms "
+                    f"step:{step}/{args.iterations}"
+                )
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+        zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+        for micro_step in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+            x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        if step < args.muon_momentum_warmup_steps:
+            # Linear warmup phase
+            frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0
+            muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+        elif args.momentum_cyclic:
+            # Cyclic momentum: triangle wave between momentum_min and momentum_max
+            period = args.momentum_cycle_period * 2
+            pos = (step % period) / period
+            if pos < 0.5:
+                muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2)
+            else:
+                muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2)
+        else:
+            muon_momentum = args.muon_momentum
+        for group in optimizer_muon.param_groups:
+            group["momentum"] = muon_momentum
+
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group["lr"] = group["base_lr"] * scale
+
+        if args.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm)
+        for opt in optimizers:
+            opt.step()
+        zero_grad_all()
+
+        step += 1
+        approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+
+        # SWA: collect checkpoints during warmdown
+        if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0:
+            if swa_state is None:
+                swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}
+                swa_count = 1
+                log0(f"swa:start step:{step}")
+            else:
+                for name, t in base_model.state_dict().items():
+                    swa_state[name] += t.detach().cpu()
+                swa_count += 1
+
+        should_log_train = (
+            args.train_log_every > 0
+            and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None)
+        )
+        if should_log_train:
+            log0(
+                f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} "
+                f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms"
+            )
+
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log0(
+        f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+
+    # Apply SWA if collected
+    if args.swa_enabled and swa_state is not None and swa_count > 1:
+        log0(f"swa:applying averaged {swa_count} checkpoints")
+        current_state = base_model.state_dict()
+        avg_state = {
+            name: (tensor / swa_count).to(dtype=current_state[name].dtype)
+            for name, tensor in swa_state.items()
+        }
+        base_model.load_state_dict(avg_state, strict=True)
+
+    # SERIALIZATION + ROUNDTRIP VALIDATION
+    if master_process:
+        torch.save(base_model.state_dict(), "final_model.pt")
+        model_bytes = os.path.getsize("final_model.pt")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model: {model_bytes} bytes")
+        log0(f"Code size: {code_bytes} bytes")
+        log0(f"Total submission size: {model_bytes + code_bytes} bytes")
+
+    # Magnitude pruning: zero out smallest weights to improve compression
+    with torch.no_grad():
+        for name, param in base_model.named_parameters():
+            if param.ndim == 2 and param.numel() > 65536:
+                threshold = torch.quantile(param.abs().float().flatten(), 0.03)
+                mask = param.abs() < threshold
+                param.masked_fill_(mask, 0.0)
+
+    # AWQ: Activation-aware weight scaling before quantization
+    awq_alpha = float(os.environ.get("AWQ_ALPHA", 0.5))
+    awq_enabled = bool(int(os.environ.get("AWQ_ENABLED", "1")))
+    if awq_enabled:
+        log0(f"awq:calibrating alpha={awq_alpha}")
+        # Step 1: Collect per-channel activation magnitudes via hooks
+        act_stats: dict[str, Tensor] = {}
+        hooks = []
+        def make_hook(name):
+            def hook_fn(module, input, output):
+                x = input[0] if isinstance(input, tuple) else input
+                if x.ndim >= 2:
+                    # Mean absolute activation per channel (last dim)
+                    s = x.detach().float().abs().mean(dim=tuple(range(x.ndim - 1)))
+                    if name in act_stats:
+                        act_stats[name] = act_stats[name] + s
+                    else:
+                        act_stats[name] = s
+            return hook_fn
+
+        for name, module in base_model.named_modules():
+            if isinstance(module, (nn.Linear, CastedLinear)):
+                hooks.append(module.register_forward_hook(make_hook(name)))
+
+        # Step 2: Run calibration batches (use val data, 4 batches)
+        base_model.eval()
+        n_calib_batches = 4
+        calib_seq_len = args.train_seq_len
+        calib_batch_seqs = 16
+        calib_tokens_per_batch = calib_batch_seqs * calib_seq_len
+        with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            for cb in range(n_calib_batches):
+                start = cb * calib_tokens_per_batch
+                end = start + calib_tokens_per_batch + 1
+                if end > val_tokens.numel():
+                    break
+                local = val_tokens[start:end].to(device=device, dtype=torch.int64)
+                x = local[:-1].reshape(-1, calib_seq_len)
+                base_model.forward_logits(x)
+
+        for h in hooks:
+            h.remove()
+
+        # Normalize activation stats
+        for name in act_stats:
+            act_stats[name] = act_stats[name] / n_calib_batches
+
+        # Step 3: Scale weight columns by s^alpha
+        awq_scales = {}
+        with torch.no_grad():
+            for name, module in base_model.named_modules():
+                if isinstance(module, (nn.Linear, CastedLinear)) and name in act_stats:
+                    s = act_stats[name].to(module.weight.device)
+                    s = s.clamp_min(1e-6)
+                    scale = s.pow(awq_alpha)
+                    # Scale weight columns (input channels)
+                    if module.weight.shape[1] == scale.shape[0]:
+                        module.weight.data = module.weight.data * scale.unsqueeze(0)
+                        awq_scales[name] = scale.cpu()
+                        # Store inverse scale to apply to inputs at inference
+                        # We'll fold this into the state dict
+
+        log0(f"awq:scaled {len(awq_scales)} layers")
+
+    # INT6 mixed quantization + zstd/zlib export
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()}
+    # Store AWQ inverse scales in the state dict for inference compensation
+    if awq_enabled and awq_scales:
+        for lname, scale in awq_scales.items():
+            sd_cpu[f"_awq_inv_scale.{lname}"] = (1.0 / scale).to(torch.float16)
+    quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"})
+    quant_buf = io.BytesIO()
+    torch.save({"w": quant_result, "m": quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    if _COMPRESSOR == "zstd":
+        quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw)
+    else:
+        quant_blob = zlib.compress(quant_raw, 9)
+    if master_process:
+        with open("final_model.int8.ptz", "wb") as f:
+            f.write(quant_blob)
+        quant_file_bytes = os.path.getsize("final_model.int8.ptz")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes")
+        log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes")
+
+    if distributed:
+        dist.barrier()
+    with open("final_model.int8.ptz", "rb") as f:
+        quant_blob_disk = f.read()
+    if _COMPRESSOR == "zstd":
+        decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk)
+    else:
+        decompressed = zlib.decompress(quant_blob_disk)
+    quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu")
+    deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu)
+    # AWQ: undo column scaling after dequantization
+    if awq_enabled:
+        awq_inv_keys = [k for k in deq_state if k.startswith("_awq_inv_scale.")]
+        for inv_key in awq_inv_keys:
+            inv_scale = deq_state.pop(inv_key).float()
+            layer_name = inv_key.replace("_awq_inv_scale.", "")
+            weight_key = layer_name + ".weight"
+            if weight_key in deq_state:
+                # Undo: W_orig = W_scaled * inv_scale (per column)
+                deq_state[weight_key] = deq_state[weight_key].float() * inv_scale.unsqueeze(0)
+                deq_state[weight_key] = deq_state[weight_key].to(dtype=sd_cpu[weight_key].dtype)
+        log0(f"awq:unscaled {len(awq_inv_keys)} layers after dequant")
+    # Remove any remaining AWQ keys before loading
+    deq_state = {k: v for k, v in deq_state.items() if not k.startswith("_awq_")}
+    base_model.load_state_dict(deq_state, strict=True)
+
+    # Sliding window eval on int6-roundtripped weights
+    torch.cuda.synchronize()
+    t_qeval = time.perf_counter()
+    if args.eval_stride > 0 and args.eval_stride < args.train_seq_len:
+        log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}")
+        q_val_loss, q_val_bpb = eval_val_sliding(
+            args, base_model, rank, world_size, device,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            stride=args.eval_stride, batch_seqs=args.eval_batch_seqs,
+        )
+    else:
+        log0("final_eval_mode:standard")
+        q_val_loss, q_val_bpb = eval_val(
+            args, model, rank, world_size, device, grad_accum_steps,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+        )
+    torch.cuda.synchronize()
+    log0(
+        f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+        f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms"
+    )
+    log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
+# fixes applied
+# tuned
+
+====================================================================================================
+Running Python 3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0]
+Running PyTorch 2.9.1+cu128
+Tue Mar 24 11:54:04 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 580.126.09             Driver Version: 580.126.09     CUDA Version: 13.0     |
++-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 80GB HBM3          On  |   00000000:19:00.0 Off |                    0 |
+| N/A   47C    P0            128W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   1  NVIDIA H100 80GB HBM3          On  |   00000000:3B:00.0 Off |                    0 |
+| N/A   37C    P0            120W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   2  NVIDIA H100 80GB HBM3          On  |   00000000:4C:00.0 Off |                    0 |
+| N/A   34C    P0            118W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   3  NVIDIA H100 80GB HBM3          On  |   00000000:5D:00.0 Off |                    0 |
+| N/A   47C    P0            134W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   4  NVIDIA H100 80GB HBM3          On  |   00000000:9B:00.0 Off |                    0 |
+| N/A   48C    P0            131W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   5  NVIDIA H100 80GB HBM3          On  |   00000000:BB:00.0 Off |                    0 |
+| N/A   38C    P0            128W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   6  NVIDIA H100 80GB HBM3          On  |   00000000:CB:00.0 Off |                    0 |
+| N/A   47C    P0            128W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+|   7  NVIDIA H100 80GB HBM3          On  |   00000000:DB:00.0 Off |                    0 |
+| N/A   36C    P0            119W /  700W |    1521MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|  No running processes found                                                             |
++-----------------------------------------------------------------------------------------+
+
+====================================================================================================
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:80
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+model_params:25517137
+world_size:8 grad_accum_steps:1
+attention_mode:gqa num_heads:8 num_kv_heads:4
+tie_embeddings:True embed_lr:0.035 matrix_lr:0.025 scalar_lr:0.025
+train_batch_tokens:786432 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:44
+warmup_step:1/20
+warmup_step:2/20
+warmup_step:3/20
+warmup_step:4/20
+warmup_step:5/20
+warmup_step:6/20
+warmup_step:7/20
+warmup_step:8/20
+warmup_step:9/20
+warmup_step:10/20
+warmup_step:11/20
+warmup_step:12/20
+warmup_step:13/20
+warmup_step:14/20
+warmup_step:15/20
+warmup_step:16/20
+warmup_step:17/20
+warmup_step:18/20
+warmup_step:19/20
+warmup_step:20/20
+step:0/20000 val_loss:6.9280 val_bpb:4.1031 train_time:0ms step_avg:0.02ms
+step:1/20000 train_loss:6.9287 train_time:149ms step_avg:149.48ms
+step:2/20000 train_loss:8.4639 train_time:222ms step_avg:111.22ms
+step:3/20000 train_loss:7.8194 train_time:320ms step_avg:106.51ms
+step:4/20000 train_loss:7.2740 train_time:418ms step_avg:104.46ms
+step:5/20000 train_loss:6.9708 train_time:515ms step_avg:102.91ms
+step:6/20000 train_loss:6.8442 train_time:613ms step_avg:102.11ms
+step:7/20000 train_loss:6.7332 train_time:710ms step_avg:101.45ms
+step:8/20000 train_loss:6.6899 train_time:808ms step_avg:100.97ms
+step:9/20000 train_loss:6.4768 train_time:904ms step_avg:100.45ms
+step:10/20000 train_loss:6.1476 train_time:1001ms step_avg:100.06ms
+step:100/20000 train_loss:3.1518 train_time:9962ms step_avg:99.62ms
+step:200/20000 train_loss:2.3792 train_time:19971ms step_avg:99.86ms
+step:300/20000 train_loss:2.5375 train_time:30002ms step_avg:100.01ms
+step:400/20000 train_loss:2.4066 train_time:40063ms step_avg:100.16ms
+step:500/20000 train_loss:2.3937 train_time:50067ms step_avg:100.13ms
+step:500/20000 val_loss:2.3569 val_bpb:1.3959 train_time:50091ms step_avg:100.18ms
+step:600/20000 train_loss:2.3337 train_time:60134ms step_avg:100.22ms
+step:700/20000 train_loss:2.3503 train_time:70193ms step_avg:100.28ms
+step:800/20000 train_loss:2.2417 train_time:80256ms step_avg:100.32ms
+step:900/20000 train_loss:2.1287 train_time:90313ms step_avg:100.35ms
+step:1000/20000 train_loss:2.2758 train_time:100310ms step_avg:100.31ms
+step:1000/20000 val_loss:2.2311 val_bpb:1.3214 train_time:100336ms step_avg:100.34ms
+step:1100/20000 train_loss:2.3322 train_time:110366ms step_avg:100.33ms
+step:1200/20000 train_loss:2.3606 train_time:120403ms step_avg:100.34ms
+step:1300/20000 train_loss:2.1112 train_time:130456ms step_avg:100.35ms
+step:1400/20000 train_loss:2.1882 train_time:140478ms step_avg:100.34ms
+step:1500/20000 train_loss:2.2263 train_time:150441ms step_avg:100.29ms
+step:1500/20000 val_loss:2.1920 val_bpb:1.2982 train_time:150469ms step_avg:100.31ms
+step:1600/20000 train_loss:2.0377 train_time:160472ms step_avg:100.30ms
+step:1700/20000 train_loss:2.1060 train_time:170501ms step_avg:100.29ms
+step:1800/20000 train_loss:2.1298 train_time:180522ms step_avg:100.29ms
+step:1900/20000 train_loss:2.1008 train_time:190493ms step_avg:100.26ms
+step:2000/20000 train_loss:2.0515 train_time:200504ms step_avg:100.25ms
+step:2000/20000 val_loss:2.1173 val_bpb:1.2540 train_time:200531ms step_avg:100.27ms
+step:2100/20000 train_loss:2.0373 train_time:210546ms step_avg:100.26ms
+step:2200/20000 train_loss:2.1344 train_time:220560ms step_avg:100.25ms
+step:2300/20000 train_loss:2.1168 train_time:230569ms step_avg:100.25ms
+step:2400/20000 train_loss:2.0723 train_time:240545ms step_avg:100.23ms
+step:2500/20000 train_loss:2.1878 train_time:250547ms step_avg:100.22ms
+step:2500/20000 val_loss:2.1235 val_bpb:1.2577 train_time:250575ms step_avg:100.23ms
+step:2600/20000 train_loss:2.1224 train_time:260544ms step_avg:100.21ms
+step:2700/20000 train_loss:2.1155 train_time:270517ms step_avg:100.19ms
+step:2800/20000 train_loss:2.1707 train_time:280521ms step_avg:100.19ms
+step:2900/20000 train_loss:2.0416 train_time:290459ms step_avg:100.16ms
+step:3000/20000 train_loss:2.1755 train_time:300453ms step_avg:100.15ms
+step:3000/20000 val_loss:2.1071 val_bpb:1.2479 train_time:300479ms step_avg:100.16ms
+step:3100/20000 train_loss:2.0540 train_time:310500ms step_avg:100.16ms
+step:3200/20000 train_loss:2.1885 train_time:320480ms step_avg:100.15ms
+step:3300/20000 train_loss:2.0829 train_time:330417ms step_avg:100.13ms
+step:3400/20000 train_loss:2.0306 train_time:340422ms step_avg:100.12ms
+step:3500/20000 train_loss:2.1900 train_time:350415ms step_avg:100.12ms
+step:3500/20000 val_loss:2.0899 val_bpb:1.2377 train_time:350441ms step_avg:100.13ms
+step:3600/20000 train_loss:2.0978 train_time:360415ms step_avg:100.12ms
+step:3700/20000 train_loss:2.1007 train_time:370408ms step_avg:100.11ms
+step:3800/20000 train_loss:2.0762 train_time:380332ms step_avg:100.09ms
+step:3900/20000 train_loss:2.0818 train_time:390308ms step_avg:100.08ms
+step:4000/20000 train_loss:1.9747 train_time:400290ms step_avg:100.07ms
+step:4000/20000 val_loss:2.0678 val_bpb:1.2247 train_time:400315ms step_avg:100.08ms
+step:4100/20000 train_loss:2.0182 train_time:410281ms step_avg:100.07ms
+step:4200/20000 train_loss:2.1542 train_time:420258ms step_avg:100.06ms
+step:4300/20000 train_loss:2.0556 train_time:430192ms step_avg:100.04ms
+step:4400/20000 train_loss:2.0332 train_time:440177ms step_avg:100.04ms
+step:4500/20000 train_loss:2.1193 train_time:450138ms step_avg:100.03ms
+step:4500/20000 val_loss:2.0431 val_bpb:1.2101 train_time:450164ms step_avg:100.04ms
+step:4600/20000 train_loss:1.8407 train_time:460123ms step_avg:100.03ms
+step:4700/20000 train_loss:2.2264 train_time:470035ms step_avg:100.01ms
+step:4800/20000 train_loss:2.4262 train_time:480013ms step_avg:100.00ms
+step:4900/20000 train_loss:2.0455 train_time:489990ms step_avg:100.00ms
+step:5000/20000 train_loss:2.0949 train_time:499981ms step_avg:100.00ms
+step:5000/20000 val_loss:2.0153 val_bpb:1.1936 train_time:500007ms step_avg:100.00ms
+step:5100/20000 train_loss:2.1149 train_time:509962ms step_avg:99.99ms
+step:5200/20000 train_loss:2.0314 train_time:519881ms step_avg:99.98ms
+step:5300/20000 train_loss:1.9922 train_time:529849ms step_avg:99.97ms
+swa:start step:5350
+step:5400/20000 train_loss:2.0348 train_time:539907ms step_avg:99.98ms
+step:5500/20000 train_loss:2.0003 train_time:549943ms step_avg:99.99ms
+step:5500/20000 val_loss:1.9868 val_bpb:1.1767 train_time:550013ms step_avg:100.00ms
+step:5600/20000 train_loss:1.9387 train_time:559996ms step_avg:100.00ms
+step:5700/20000 train_loss:1.9929 train_time:569977ms step_avg:100.00ms
+step:5800/20000 train_loss:1.9749 train_time:580010ms step_avg:100.00ms
+step:5900/20000 train_loss:1.8845 train_time:590024ms step_avg:100.00ms
+step:6000/20000 train_loss:1.9269 train_time:600029ms step_avg:100.00ms
+step:6000/20000 val_loss:1.9619 val_bpb:1.1619 train_time:600099ms step_avg:100.02ms
+stopping_early: wallclock_cap train_time:600099ms step:6000/20000
+peak memory allocated: 20841 MiB reserved: 21060 MiB
+swa:applying averaged 14 checkpoints
+Serialized model: 98437419 bytes
+Code size: 58616 bytes
+Total submission size: 98496035 bytes
+awq:calibrating alpha=0.5
+awq:scaled 61 layers
+Serialized model int6+zstd: 15367341 bytes
+Total submission size int8+zlib: 15425957 bytes
+awq:unscaled 61 layers after dequant
+final_eval_mode:sliding_window stride:64 batch_seqs:64
+final_int8_zlib_roundtrip val_loss:1.9460 val_bpb:1.1526 eval_time:179947ms
+final_int8_zlib_roundtrip_exact val_loss:1.94603275 val_bpb:1.15255326
diff --git a/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/run.sh b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/run.sh
new file mode 100755
index 000000000..8f4429392
--- /dev/null
+++ b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/run.sh
@@ -0,0 +1,79 @@
+#!/bin/bash
+# ============================================================
+# SUBMISSION: exp18 AWQ + cyclic momentum + relu_sq + 11L shared
+# 8×H100 SXM, 3 seeds, full sliding window eval
+# ============================================================
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+EXP_NAME="submission_exp18_awq-cyclic-relusq-11Lshared_8xH100"
+LOG_DIR="records/h100_experiments/${EXP_NAME}/logs"
+
+cd /workspace/parameter-golf
+mkdir -p "${LOG_DIR}"
+
+# --- Architecture ---
+export NUM_LAYERS=11
+export UNIQUE_LAYERS=10
+export MODEL_DIM=512
+export NUM_HEADS=8
+export NUM_KV_HEADS=4
+export MLP_MULT=3.0
+export MLP_ACTIVATION=relu_sq
+export VOCAB_SIZE=1024
+export TIE_EMBEDDINGS=1
+export LOGIT_SOFTCAP=30.0
+export BIGRAM_VOCAB_SIZE=10240
+export BIGRAM_DIM=128
+
+# --- Training (8×H100 scale) ---
+export ITERATIONS=20000
+export WARMUP_STEPS=20
+export WARMDOWN_ITERS=3500
+export TRAIN_BATCH_TOKENS=786432
+export TRAIN_SEQ_LEN=2048
+export MAX_WALLCLOCK_SECONDS=600
+
+# --- Optimizer ---
+export MATRIX_LR=0.025
+export SCALAR_LR=0.025
+export TIED_EMBED_LR=0.035
+export MUON_MOMENTUM=0.99
+export MUON_MOMENTUM_WARMUP_START=0.92
+export MUON_MOMENTUM_WARMUP_STEPS=1500
+export MOMENTUM_CYCLIC=1
+export MOMENTUM_MIN=0.85
+export MOMENTUM_MAX=0.95
+export MOMENTUM_CYCLE_PERIOD=50
+export GRAD_CLIP_NORM=0.3
+export WEIGHT_DECAY=0.04
+
+# --- SWA ---
+export SWA_ENABLED=1
+export SWA_START_FRAC=0.2
+export SWA_EVERY=50
+
+# --- Validation & Eval ---
+export VAL_LOSS_EVERY=500
+export VAL_BATCH_SIZE=524288
+export TRAIN_LOG_EVERY=100
+export EVAL_STRIDE=64
+export EVAL_BATCH_SEQS=64
+
+# --- AWQ ---
+export AWQ_ENABLED=1
+export AWQ_ALPHA=0.5
+
+# --- Run 3 seeds ---
+for SEED in 42 43 44; do
+    export SEED
+    export RUN_ID="${EXP_NAME}_seed${SEED}"
+    echo "============================================"
+    echo "=== SEED ${SEED} ==="
+    echo "============================================"
+    torchrun --nproc_per_node=8 "${SCRIPT_DIR}/train_gpt.py" 2>&1 | tee "${LOG_DIR}/${EXP_NAME}_seed${SEED}.log"
+    echo "=== SEED ${SEED} COMPLETE ==="
+    echo ""
+done
+
+echo "=== ALL 3 SEEDS COMPLETE ==="
diff --git a/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/setup_and_run.sh b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/setup_and_run.sh
new file mode 100755
index 000000000..3a7fa0e4e
--- /dev/null
+++ b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/setup_and_run.sh
@@ -0,0 +1,61 @@
+#!/bin/bash
+# ============================================================
+# FULL SETUP + RUN for 8×H100 submission
+# Usage: Pass SSH host and port as arguments
+#   ./setup_and_run.sh <host> <port>
+# ============================================================
+set -euo pipefail
+
+HOST="${1:?Usage: $0 <host> <port>}"
+PORT="${2:?Usage: $0 <host> <port>}"
+SSH="ssh -o StrictHostKeyChecking=no -p ${PORT} root@${HOST}"
+SCP="scp -P ${PORT}"
+LOCAL_BASE="/Users/sidhantthole/Documents/llama_index_exp/openaigolf/parameter-golf"
+REMOTE_BASE="/workspace/parameter-golf"
+EXP="submission_exp18_awq-cyclic-relusq-11Lshared_8xH100"
+
+echo "=== Step 1: Test connection ==="
+$SSH "echo 'Connected!' && nvidia-smi --query-gpu=name,memory.total --format=csv,noheader"
+
+echo "=== Step 2: Clone repo + install deps ==="
+$SSH "cd /workspace && rm -rf parameter-golf && git clone https://github.com/openai/parameter-golf.git && pip install --break-system-packages -q zstandard"
+
+echo "=== Step 3: Start data download in background ==="
+$SSH "cd ${REMOTE_BASE} && nohup python3 data/cached_challenge_fineweb.py --variant sp1024 > /tmp/data_download.out 2>&1 &"
+
+echo "=== Step 4: Copy experiment files while data downloads ==="
+$SSH "mkdir -p ${REMOTE_BASE}/records/h100_experiments"
+$SCP -r "${LOCAL_BASE}/records/h100_experiments/${EXP}" "root@${HOST}:${REMOTE_BASE}/records/h100_experiments/"
+$SSH "chmod +x ${REMOTE_BASE}/records/h100_experiments/${EXP}/run.sh"
+
+echo "=== Step 5: Wait for data download ==="
+while true; do
+    COUNT=$($SSH "ls ${REMOTE_BASE}/data/datasets/fineweb10B_sp1024/fineweb_train_*.bin 2>/dev/null | wc -l" 2>/dev/null || echo "0")
+    echo "  Data shards: ${COUNT}/80"
+    if [ "$COUNT" -ge 80 ]; then
+        break
+    fi
+    sleep 10
+done
+echo "Data download complete!"
+
+echo "=== Step 6: Launch submission (3 seeds) ==="
+$SSH "nohup bash ${REMOTE_BASE}/records/h100_experiments/${EXP}/run.sh > /tmp/submission.out 2>&1 &"
+echo "Submission launched!"
+
+echo "=== Step 7: Syncing records every 10 seconds ==="
+while true; do
+    rsync -az -e "ssh -p ${PORT}" "root@${HOST}:${REMOTE_BASE}/records/h100_experiments/${EXP}/" "${LOCAL_BASE}/records/h100_experiments/${EXP}/" 2>/dev/null
+    rsync -az -e "ssh -p ${PORT}" "root@${HOST}:${REMOTE_BASE}/logs/" "${LOCAL_BASE}/logs/" 2>/dev/null
+
+    # Check if still running
+    RUNNING=$($SSH "ps aux | grep train_gpt | grep -v grep | wc -l" 2>/dev/null || echo "0")
+    if [ "$RUNNING" -eq 0 ]; then
+        # Final sync
+        rsync -az -e "ssh -p ${PORT}" "root@${HOST}:${REMOTE_BASE}/records/h100_experiments/${EXP}/" "${LOCAL_BASE}/records/h100_experiments/${EXP}/" 2>/dev/null
+        rsync -az -e "ssh -p ${PORT}" "root@${HOST}:${REMOTE_BASE}/logs/" "${LOCAL_BASE}/logs/" 2>/dev/null
+        echo "=== ALL SEEDS COMPLETE ==="
+        break
+    fi
+    sleep 10
+done
diff --git a/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/submission.json b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/submission.json
new file mode 100644
index 000000000..08b8e425c
--- /dev/null
+++ b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/submission.json
@@ -0,0 +1,11 @@
+{
+  "author": "SPThole",
+  "github_id": "SPThole",
+  "name": "AWQ + Cyclic Momentum + ReLU² + 11L Shared",
+  "blurb": "Activation-Aware Weight Quantization (AWQ) closes the int5/int6 quant gap from 0.027 to 0.010 bpb. Cyclic Muon momentum (0.85-0.95 triangle wave) escapes sharp minima. ReLU² for sparser MLPs. 11 layers with 10 unique weights. Emerged from 21+ systematic experiments on 1×H100/A40 before scaling to 8×H100.",
+  "date": "2026-03-25T00:00:00Z",
+  "val_loss": 1.94293025,
+  "val_bpb": 1.15071578,
+  "bytes_total": 15465308,
+  "bytes_code": 58616
+}
diff --git a/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/train_gpt.py b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/train_gpt.py
new file mode 100644
index 000000000..5645ab670
--- /dev/null
+++ b/records/track_10min_16mb/2025-03-24_AWQ_CyclMom_11L_shared/train_gpt.py
@@ -0,0 +1,1340 @@
+"""
+The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder.
+
+Hard stop: To keep readable for newcomers, let's make sure `train_gpt.py` and `train_gpt_mlx.py` never are longer than 1500 lines.
+"""
+
+from __future__ import annotations
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import time
+import uuid
+import zlib
+from pathlib import Path
+
+try:
+    import zstandard
+    _COMPRESSOR = "zstd"
+except ImportError:
+    _COMPRESSOR = "zlib"
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+# -----------------------------
+# HYPERPARAMETERS
+# -----------------------------
+
+class Hyperparameters:
+    data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024")
+    train_files = os.path.join(data_path, "fineweb_train_*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_*.bin")
+    tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model")
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+    seed = int(os.environ.get("SEED", 42))
+
+    val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288))
+    val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500))
+    train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100))
+
+    iterations = int(os.environ.get("ITERATIONS", 20000))
+    warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000))
+    warmup_steps = int(os.environ.get("WARMUP_STEPS", 20))
+    train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432))
+    train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048))
+    max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0))
+    qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5))
+
+    vocab_size = int(os.environ.get("VOCAB_SIZE", 1024))
+    num_layers = int(os.environ.get("NUM_LAYERS", 11))
+    unique_layers = int(os.environ.get("UNIQUE_LAYERS", 10))
+    num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4))
+    model_dim = int(os.environ.get("MODEL_DIM", 512))
+    num_heads = int(os.environ.get("NUM_HEADS", 8))
+    mlp_mult = float(os.environ.get("MLP_MULT", 3.0))
+    mlp_activation = os.environ.get("MLP_ACTIVATION", "relu_sq")
+    tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1")))
+    rope_base = float(os.environ.get("ROPE_BASE", 10000.0))
+    logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0))
+
+    embed_lr = float(os.environ.get("EMBED_LR", 0.6))
+    head_lr = float(os.environ.get("HEAD_LR", 0.008))
+    tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03))
+    tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005))
+    matrix_lr = float(os.environ.get("MATRIX_LR", 0.02))
+    scalar_lr = float(os.environ.get("SCALAR_LR", 0.02))
+    muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99))
+    muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5))
+    muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500))
+    momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1")))
+    momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85))
+    momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95))
+    momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50))
+    beta1 = float(os.environ.get("BETA1", 0.9))
+    beta2 = float(os.environ.get("BETA2", 0.95))
+    adam_eps = float(os.environ.get("ADAM_EPS", 1e-8))
+    grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3))
+    weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04))
+
+    eval_stride = int(os.environ.get("EVAL_STRIDE", 64))
+    eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32))
+
+    bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240))
+    bigram_dim = int(os.environ.get("BIGRAM_DIM", 128))
+
+    swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1")))
+    swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4))
+    swa_every = int(os.environ.get("SWA_EVERY", 50))
+
+# -----------------------------
+# MUON OPTIMIZER
+# -----------------------------
+
+def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, weight_decay: float = 0.0):
+        super().__init__(
+            params,
+            dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, weight_decay=weight_decay),
+        )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr = group["lr"]
+            momentum = group["momentum"]
+            backend_steps = group["backend_steps"]
+            nesterov = group["nesterov"]
+
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr : curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+
+            wd = group.get("weight_decay", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.data.mul_(1.0 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+
+# -----------------------------
+# TOKENIZER-AGNOSTIC EVALUATION
+# -----------------------------
+
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}")
+    return tokens[: usable + 1]
+
+
+def eval_val(
+    args: Hyperparameters,
+    model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    grad_accum_steps: int,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+) -> tuple[float, float]:
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    if local_batch_tokens < args.train_seq_len:
+        raise ValueError(
+            "VAL_BATCH_SIZE must provide at least one sequence per rank; "
+            f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, "
+            f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}"
+        )
+    local_batch_seqs = local_batch_tokens // args.train_seq_len
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * args.train_seq_len
+            raw_end = batch_seq_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, args.train_seq_len)
+            y = local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+    val_loss = val_loss_sum / val_token_count
+    bits_per_token = val_loss.item() / math.log(2.0)
+    tokens_per_byte = val_token_count.item() / val_byte_count.item()
+    model.train()
+    return float(val_loss.item()), float(bits_per_token * tokens_per_byte)
+
+
+# -----------------------------
+# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed)
+# -----------------------------
+
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "CONTROL_TENSOR_NAME_PATTERNS",
+        "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,bigram.scale",
+    ).split(",")
+    if pattern
+)
+FP16_KEEP_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS",
+        ",".join(CONTROL_TENSOR_NAME_PATTERNS),
+    ).split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_MAX_NUMEL = 65_536
+INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_PERCENTILE = 99.99984
+INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0
+
+def tensor_nbytes(t: Tensor) -> int:
+    return int(t.numel()) * int(t.element_size())
+
+def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        clip_abs = (
+            torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1)
+            if t32.numel()
+            else torch.empty((t32.shape[0],), dtype=torch.float32)
+        )
+        clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None])
+        scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0)
+        q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous()
+        return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+    clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous()
+    return q, scale
+
+
+def _classify_param(name: str) -> str:
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if "bigram" in name:
+        return "bigram"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+
+def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        row_max = t32.abs().amax(dim=1)
+        scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16)
+        scale = scale.clamp_min(torch.finfo(torch.float16).tiny)
+        q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8)
+        return q, scale
+    amax = t32.abs().max().item()
+    scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16)
+    q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8)
+    return q, scale
+
+def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]):
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = _classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 8192:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS):
+            result[name] = t.to(dtype=torch.float16).contiguous()
+            meta[name] = "passthrough_fp16"
+            continue
+        if cat in int6_cats and t.ndim >= 1:
+            clip = 15 if cat == "mlp" else 31  # int5 for MLP, int6 for attention
+            q, s = quantize_intN_per_row(t, clip_range=clip)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    return result, meta
+
+def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object],
+                          template_sd: dict[str, Tensor]) -> dict[str, Tensor]:
+    out: dict[str, Tensor] = {}
+    for name, orig in template_sd.items():
+        info = meta[name]
+        orig_dtype = orig.dtype
+        if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"):
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + ".q"], result[name + ".scale"]
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+
+
+# -----------------------------
+# DATA LOADING
+# -----------------------------
+
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    expected_size = header_bytes + num_tokens * token_bytes
+    if file.stat().st_size != expected_size:
+        raise ValueError(f"Shard size mismatch for {file}: expected {expected_size} bytes")
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    if tokens_np.size != num_tokens:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = load_data_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self) -> None:
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = load_data_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks: list[Tensor] = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos : self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start : start + per_rank_span].to(dtype=torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+
+# -----------------------------
+# TRANSFORMER MODULES
+# -----------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+
+class CastedLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, None, :, :]
+            self._sin_cached = freqs.sin()[None, None, :, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        if dim % num_heads != 0:
+            raise ValueError("model_dim must be divisible by num_heads")
+        if num_heads % num_kv_heads != 0:
+            raise ValueError("num_heads must be divisible by num_kv_heads")
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        if self.head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin)
+        k = apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None]
+        y = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=None, is_causal=True,
+            enable_gqa=(self.num_kv_heads != self.num_heads),
+        )
+        y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, mlp_mult: float):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = torch.relu(self.fc(x))
+        return self.proj(x.square())
+
+
+class SmearGate(nn.Module):
+    """Blend each token's embedding with the previous token's embedding."""
+    def __init__(self, dim: int):
+        super().__init__()
+        self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :]
+        x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1)
+        return (1 - g) * x + g * x_prev
+
+
+class BigramHashEmbedding(nn.Module):
+    """Hash consecutive token pairs into a learned embedding table."""
+    def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int):
+        super().__init__()
+        self.bigram_vocab_size = bigram_vocab_size
+        self.embed = nn.Embedding(bigram_vocab_size, bigram_dim)
+        nn.init.zeros_(self.embed.weight)
+        self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32))
+
+    def bigram_hash(self, tokens: Tensor) -> Tensor:
+        t = tokens.to(torch.int32)
+        mod = self.bigram_vocab_size - 1
+        out = torch.empty_like(t)
+        out[..., 0] = mod
+        out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod
+        return out.long()
+
+    def forward(self, token_ids: Tensor) -> Tensor:
+        h = self.embed(self.bigram_hash(token_ids))
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x))
+        x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out
+        x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        num_layers: int,
+        model_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        mlp_mult: float,
+        tie_embeddings: bool,
+        tied_embed_init_std: float,
+        logit_softcap: float,
+        rope_base: float,
+        qk_gain_init: float,
+        bigram_vocab_size: int = 0,
+        bigram_dim: int = 128,
+        unique_layers: int = 0,
+    ):
+        super().__init__()
+        if logit_softcap <= 0.0:
+            raise ValueError(f"logit_softcap must be positive, got {logit_softcap}")
+        self.tie_embeddings = tie_embeddings
+        self.tied_embed_init_std = tied_embed_init_std
+        self.logit_softcap = logit_softcap
+        self.tok_emb = nn.Embedding(vocab_size, model_dim)
+        self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+        self.smear = SmearGate(model_dim)
+        # Layer sharing: create unique_layers physical blocks, map num_layers virtual layers to them
+        n_unique = unique_layers if unique_layers > 0 else num_layers
+        self.blocks = nn.ModuleList(
+            [
+                Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init)
+                for _ in range(n_unique)
+            ]
+        )
+        # Mapping from virtual layer index → physical block index
+        # Last virtual layer shares with the last physical block
+        self._layer_map = list(range(min(n_unique, num_layers)))
+        while len(self._layer_map) < num_layers:
+            self._layer_map.append(n_unique - 1)
+        self.final_norm = RMSNorm()
+        self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        num_layers = len(self._layer_map)  # virtual depth, not physical blocks
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, "_zero_init", False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+                    if ".proj." in name or name.endswith(".proj"):
+                        with torch.no_grad():
+                            module.weight.mul_(1.0 / math.sqrt(2 * num_layers))
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x).reshape(-1, x.size(-1))
+        targets = target_ids.reshape(-1)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            if self.lm_head is None:
+                raise RuntimeError("lm_head is required when tie_embeddings=False")
+            logits_proj = self.lm_head(x)
+        logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+        return F.cross_entropy(logits.float(), targets, reduction="mean")
+
+    def forward_logits(self, input_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+
+
+def eval_val_sliding(
+    args: Hyperparameters,
+    base_model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+    stride: int,
+    batch_seqs: int = 32,
+) -> tuple[float, float]:
+    seq_len = args.train_seq_len
+    total_tokens = val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, stride)
+                     if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0]
+    total_windows = len(window_starts)
+    my_s = (total_windows * rank) // world_size
+    my_e = (total_windows * (rank + 1)) // world_size
+    my_windows = window_starts[my_s:my_e]
+
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    base_model.eval()
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens: list[int] = []
+            for i, ws in enumerate(batch_ws):
+                end = min(ws + seq_len, total_tokens)
+                wlen = end - ws
+                wlens.append(wlen)
+                chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits = base_model.forward_logits(x_batch)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else max(wlen - stride, 0)
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = base_bytes_lut[tgt].to(torch.float64)
+                tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+            if rank == 0 and (bi // batch_seqs) % 50 == 0:
+                done = min(bi + batch_seqs, len(my_windows))
+                pct = done / len(my_windows) * 100
+                running_bpb = 0.0
+                if token_count.item() > 0:
+                    rl = (loss_sum / token_count).item()
+                    running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item())
+                print(f"  sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True)
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    val_loss = (loss_sum / token_count).item()
+    bits_per_token = val_loss / math.log(2.0)
+    tokens_per_byte = token_count.item() / byte_count.item()
+    base_model.train()
+    return val_loss, bits_per_token * tokens_per_byte
+
+
+# -----------------------------
+# TRAINING
+# -----------------------------
+
+def main() -> None:
+    global zeropower_via_newtonschulz5
+
+    code = Path(__file__).read_text(encoding="utf-8")
+    args = Hyperparameters()
+    zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral")
+    grad_accum_steps = 8 // world_size
+    grad_scale = 1.0 / grad_accum_steps
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+
+    logfile = None
+    if master_process:
+        os.makedirs("logs", exist_ok=True)
+        logfile = f"logs/{args.run_id}.txt"
+        print(logfile)
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile is not None:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+    log0(f"Running Python {sys.version}", console=False)
+    log0(f"Running PyTorch {torch.__version__}", console=False)
+    log0(
+        subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout,
+        console=False,
+    )
+    log0("=" * 100, console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    if not args.tokenizer_path.endswith(".model"):
+        raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}")
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    if int(sp.vocab_size()) != args.vocab_size:
+        raise ValueError(
+            f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}"
+        )
+    dataset_dir = Path(args.data_path).resolve()
+    actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin")))
+    val_tokens = load_validation_tokens(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(
+        sp, args.vocab_size, device
+    )
+    log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}")
+    log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}")
+    log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}")
+
+    # MODEL + OPTIMIZER SETUP
+    base_model = GPT(
+        vocab_size=args.vocab_size,
+        num_layers=args.num_layers,
+        model_dim=args.model_dim,
+        num_heads=args.num_heads,
+        num_kv_heads=args.num_kv_heads,
+        mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings,
+        tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap,
+        rope_base=args.rope_base,
+        qk_gain_init=args.qk_gain_init,
+        bigram_vocab_size=args.bigram_vocab_size,
+        bigram_dim=args.bigram_dim,
+        unique_layers=args.unique_layers,
+    ).to(device).bfloat16()
+    for module in base_model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model
+
+    block_named_params = list(base_model.blocks.named_parameters())
+    matrix_params = [
+        p for name, p in block_named_params
+        if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    scalar_params = [
+        p for name, p in block_named_params
+        if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    if base_model.skip_weights.numel() > 0:
+        scalar_params.append(base_model.skip_weights)
+    scalar_params.append(base_model.smear.gate)
+    if base_model.bigram is not None:
+        scalar_params.append(base_model.bigram.scale)
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}]
+    if base_model.bigram is not None:
+        tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr})
+        if base_model.bigram.proj is not None:
+            matrix_params.append(base_model.bigram.proj.weight)
+
+    optimizer_tok = torch.optim.AdamW(
+        tok_params,
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizer_muon = Muon(
+        matrix_params,
+        lr=args.matrix_lr,
+        momentum=args.muon_momentum,
+        backend_steps=args.muon_backend_steps,
+        weight_decay=0.04,
+    )
+    for group in optimizer_muon.param_groups:
+        group["base_lr"] = args.matrix_lr
+    optimizer_scalar = torch.optim.AdamW(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar]
+    if base_model.lm_head is not None:
+        optimizer_head = torch.optim.Adam(
+            [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}],
+            betas=(args.beta1, args.beta2),
+            eps=args.adam_eps,
+            fused=True,
+        )
+        optimizers.insert(1, optimizer_head)
+
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"model_params:{n_params}")
+    log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}")
+    log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}")
+    log0(
+        f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} "
+        f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}"
+    )
+    log0(
+        f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} "
+        f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} "
+        f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}"
+    )
+    log0(f"seed:{args.seed}")
+
+    # DATA LOADER & MODEL WARMUP
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all() -> None:
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float) -> float:
+        if args.warmdown_iters <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = max(args.iterations - args.warmdown_iters, 0)
+            return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0
+        step_ms = elapsed_ms / max(step, 1)
+        warmdown_ms = args.warmdown_iters * step_ms
+        remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+        return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+
+    if args.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(args.warmup_steps):
+            zero_grad_all()
+            for micro_step in range(grad_accum_steps):
+                if distributed:
+                    model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+                x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                    warmup_loss = model(x, y)
+                (warmup_loss * grad_scale).backward()
+            for opt in optimizers:
+                opt.step()
+            zero_grad_all()
+            if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps:
+                log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}")
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        zero_grad_all()
+        if distributed:
+            model.require_backward_grad_sync = True
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # MAIN TRAINING LOOP
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    swa_state: dict[str, Tensor] | None = None
+    swa_count = 0
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    step = 0
+    while True:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            )
+            log0(
+                f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms"
+            )
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(
+                    f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms "
+                    f"step:{step}/{args.iterations}"
+                )
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+        zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+        for micro_step in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+            x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        if step < args.muon_momentum_warmup_steps:
+            # Linear warmup phase
+            frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0
+            muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+        elif args.momentum_cyclic:
+            # Cyclic momentum: triangle wave between momentum_min and momentum_max
+            period = args.momentum_cycle_period * 2
+            pos = (step % period) / period
+            if pos < 0.5:
+                muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2)
+            else:
+                muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2)
+        else:
+            muon_momentum = args.muon_momentum
+        for group in optimizer_muon.param_groups:
+            group["momentum"] = muon_momentum
+
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group["lr"] = group["base_lr"] * scale
+
+        if args.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm)
+        for opt in optimizers:
+            opt.step()
+        zero_grad_all()
+
+        step += 1
+        approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+
+        # SWA: collect checkpoints during warmdown
+        if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0:
+            if swa_state is None:
+                swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}
+                swa_count = 1
+                log0(f"swa:start step:{step}")
+            else:
+                for name, t in base_model.state_dict().items():
+                    swa_state[name] += t.detach().cpu()
+                swa_count += 1
+
+        should_log_train = (
+            args.train_log_every > 0
+            and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None)
+        )
+        if should_log_train:
+            log0(
+                f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} "
+                f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms"
+            )
+
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log0(
+        f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+
+    # Apply SWA if collected
+    if args.swa_enabled and swa_state is not None and swa_count > 1:
+        log0(f"swa:applying averaged {swa_count} checkpoints")
+        current_state = base_model.state_dict()
+        avg_state = {
+            name: (tensor / swa_count).to(dtype=current_state[name].dtype)
+            for name, tensor in swa_state.items()
+        }
+        base_model.load_state_dict(avg_state, strict=True)
+
+    # SERIALIZATION + ROUNDTRIP VALIDATION
+    if master_process:
+        torch.save(base_model.state_dict(), "final_model.pt")
+        model_bytes = os.path.getsize("final_model.pt")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model: {model_bytes} bytes")
+        log0(f"Code size: {code_bytes} bytes")
+        log0(f"Total submission size: {model_bytes + code_bytes} bytes")
+
+    # Magnitude pruning: zero out smallest weights to improve compression
+    with torch.no_grad():
+        for name, param in base_model.named_parameters():
+            if param.ndim == 2 and param.numel() > 65536:
+                threshold = torch.quantile(param.abs().float().flatten(), 0.03)
+                mask = param.abs() < threshold
+                param.masked_fill_(mask, 0.0)
+
+    # AWQ: Activation-aware weight scaling before quantization
+    awq_alpha = float(os.environ.get("AWQ_ALPHA", 0.5))
+    awq_enabled = bool(int(os.environ.get("AWQ_ENABLED", "1")))
+    if awq_enabled:
+        log0(f"awq:calibrating alpha={awq_alpha}")
+        # Step 1: Collect per-channel activation magnitudes via hooks
+        act_stats: dict[str, Tensor] = {}
+        hooks = []
+        def make_hook(name):
+            def hook_fn(module, input, output):
+                x = input[0] if isinstance(input, tuple) else input
+                if x.ndim >= 2:
+                    # Mean absolute activation per channel (last dim)
+                    s = x.detach().float().abs().mean(dim=tuple(range(x.ndim - 1)))
+                    if name in act_stats:
+                        act_stats[name] = act_stats[name] + s
+                    else:
+                        act_stats[name] = s
+            return hook_fn
+
+        for name, module in base_model.named_modules():
+            if isinstance(module, (nn.Linear, CastedLinear)):
+                hooks.append(module.register_forward_hook(make_hook(name)))
+
+        # Step 2: Run calibration batches (use val data, 4 batches)
+        base_model.eval()
+        n_calib_batches = 4
+        calib_seq_len = args.train_seq_len
+        calib_batch_seqs = 16
+        calib_tokens_per_batch = calib_batch_seqs * calib_seq_len
+        with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            for cb in range(n_calib_batches):
+                start = cb * calib_tokens_per_batch
+                end = start + calib_tokens_per_batch + 1
+                if end > val_tokens.numel():
+                    break
+                local = val_tokens[start:end].to(device=device, dtype=torch.int64)
+                x = local[:-1].reshape(-1, calib_seq_len)
+                base_model.forward_logits(x)
+
+        for h in hooks:
+            h.remove()
+
+        # Normalize activation stats
+        for name in act_stats:
+            act_stats[name] = act_stats[name] / n_calib_batches
+
+        # Step 3: Scale weight columns by s^alpha
+        awq_scales = {}
+        with torch.no_grad():
+            for name, module in base_model.named_modules():
+                if isinstance(module, (nn.Linear, CastedLinear)) and name in act_stats:
+                    s = act_stats[name].to(module.weight.device)
+                    s = s.clamp_min(1e-6)
+                    scale = s.pow(awq_alpha)
+                    # Scale weight columns (input channels)
+                    if module.weight.shape[1] == scale.shape[0]:
+                        module.weight.data = module.weight.data * scale.unsqueeze(0)
+                        awq_scales[name] = scale.cpu()
+                        # Store inverse scale to apply to inputs at inference
+                        # We'll fold this into the state dict
+
+        log0(f"awq:scaled {len(awq_scales)} layers")
+
+    # INT6 mixed quantization + zstd/zlib export
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()}
+    # Store AWQ inverse scales in the state dict for inference compensation
+    if awq_enabled and awq_scales:
+        for lname, scale in awq_scales.items():
+            sd_cpu[f"_awq_inv_scale.{lname}"] = (1.0 / scale).to(torch.float16)
+    quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"})
+    quant_buf = io.BytesIO()
+    torch.save({"w": quant_result, "m": quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    if _COMPRESSOR == "zstd":
+        quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw)
+    else:
+        quant_blob = zlib.compress(quant_raw, 9)
+    if master_process:
+        with open("final_model.int8.ptz", "wb") as f:
+            f.write(quant_blob)
+        quant_file_bytes = os.path.getsize("final_model.int8.ptz")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes")
+        log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes")
+
+    if distributed:
+        dist.barrier()
+    with open("final_model.int8.ptz", "rb") as f:
+        quant_blob_disk = f.read()
+    if _COMPRESSOR == "zstd":
+        decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk)
+    else:
+        decompressed = zlib.decompress(quant_blob_disk)
+    quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu")
+    deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu)
+    # AWQ: undo column scaling after dequantization
+    if awq_enabled:
+        awq_inv_keys = [k for k in deq_state if k.startswith("_awq_inv_scale.")]
+        for inv_key in awq_inv_keys:
+            inv_scale = deq_state.pop(inv_key).float()
+            layer_name = inv_key.replace("_awq_inv_scale.", "")
+            weight_key = layer_name + ".weight"
+            if weight_key in deq_state:
+                # Undo: W_orig = W_scaled * inv_scale (per column)
+                deq_state[weight_key] = deq_state[weight_key].float() * inv_scale.unsqueeze(0)
+                deq_state[weight_key] = deq_state[weight_key].to(dtype=sd_cpu[weight_key].dtype)
+        log0(f"awq:unscaled {len(awq_inv_keys)} layers after dequant")
+    # Remove any remaining AWQ keys before loading
+    deq_state = {k: v for k, v in deq_state.items() if not k.startswith("_awq_")}
+    base_model.load_state_dict(deq_state, strict=True)
+
+    # Sliding window eval on int6-roundtripped weights
+    torch.cuda.synchronize()
+    t_qeval = time.perf_counter()
+    if args.eval_stride > 0 and args.eval_stride < args.train_seq_len:
+        log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}")
+        q_val_loss, q_val_bpb = eval_val_sliding(
+            args, base_model, rank, world_size, device,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            stride=args.eval_stride, batch_seqs=args.eval_batch_seqs,
+        )
+    else:
+        log0("final_eval_mode:standard")
+        q_val_loss, q_val_bpb = eval_val(
+            args, model, rank, world_size, device, grad_accum_steps,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+        )
+    torch.cuda.synchronize()
+    log0(
+        f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+        f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms"
+    )
+    log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
+# fixes applied
+# tuned
diff --git a/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/README.md b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/README.md
new file mode 100644
index 000000000..5cd5744bb
--- /dev/null
+++ b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/README.md
@@ -0,0 +1,70 @@
+# Co-occurrence QK Initialization
+
+## Score: val_bpb = 1.3525 (1×H100, single seed)
+
+Trained on 1×H100 80GB in 600 seconds. 15.55MB artifact (int6+zstd). Run on 1×H100 due to compute constraints. Built upon [PR #623](https://github.com/openai/parameter-golf/pull/623).
+
+## Approach
+
+Initializes W_Q and W_K in layer 0 from bigram co-occurrence statistics so that the initial attention pattern reflects real token relationships rather than random noise.
+
+### Mathematical formulation
+
+**Goal**: At step 0, hidden states h ≈ E[token_id] (just the embedding). We want the attention logit between tokens i and j to approximate their co-occurrence:
+
+```
+q_i · k_j = (h_i W_Q^T) · (W_K h_j) ≈ C[tok_i, tok_j]
+```
+
+where C is the 1024×1024 bigram co-occurrence matrix.
+
+**Step 1 — Build co-occurrence matrix**: Scan 2M training tokens. For each consecutive pair (t_i, t_{i+1}), increment C[t_i, t_{i+1}]. Apply log-transform: C ← log(C + 1) then center (subtract row/column means). This gives a PMI-like matrix.
+
+**Step 2 — Project into model dimension**: Since W_Q and W_K operate on model_dim (512), not vocab_size (1024), we project C into model space via a fixed random matrix P ∈ R^{1024×512}:
+
+```
+C_proj = P^T C P    ∈ R^{512×512}
+```
+
+**Step 3 — SVD factorization**: Decompose C_proj = U S V^T. Take top d_head (64) components:
+
+```
+W_Q ← (U[:, :q_dim] · diag(√S[:q_dim]))^T    ∈ R^{q_dim × 512}
+W_K ← (V[:k_dim, :] · diag(√S[:k_dim]))^T     ∈ R^{k_dim × 512}
+```
+
+This ensures W_Q^T W_K ≈ C_proj (scaled), so Q·K^T at step 0 reflects co-occurrence.
+
+**Step 4 — Scale normalization**: Rescale W_Q and W_K to match the norm of the default orthogonal initialization, preventing gradient scale mismatch:
+
+```
+W_Q ← W_Q · (‖W_Q_orig‖ / ‖W_Q‖)
+W_K ← W_K · (‖W_K_orig‖ / ‖W_K‖)
+```
+
+**Head diversity**: With 8 heads (head_dim=64), SVD components 1–64 go to head 0, 65–128 to head 1, etc. Each head captures a different slice of co-occurrence structure.
+
+Zero extra parameters — only changes initialization. Co-occurrence computation takes <3s.
+
+## Hyperparameters
+
+| Parameter | Value |
+|-----------|-------|
+| num_layers | 11 (10 unique) |
+| model_dim | 512 |
+| mlp_activation | ReLU² |
+| cooc_init_tokens | 2,000,000 |
+| cooc_init_layer | 0 only |
+| train_batch_tokens | 524,288 |
+| matrix_lr / scalar_lr | 0.025 |
+| swa_every | 50, start_frac=0.2 |
+
+## Key Metrics
+
+- **val_bpb: 1.3525** (post int6+zstd roundtrip)
+- Pre-quant val_bpb: 1.3245
+- Quantization penalty: 0.0280 bpb
+- Training: 1,099 steps in 600s (546 ms/step)
+- Artifact size: 15,545,987 bytes (15.55MB)
+- SWA: averaged 12 checkpoints
+- Peak memory: 14,656 MiB
diff --git a/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/logs.txt b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/logs.txt
new file mode 100644
index 000000000..bc8128fdd
--- /dev/null
+++ b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/logs.txt
@@ -0,0 +1,1572 @@
+"""
+The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder.
+
+Hard stop: To keep readable for newcomers, let's make sure `train_gpt.py` and `train_gpt_mlx.py` never are longer than 1500 lines.
+"""
+
+from __future__ import annotations
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import time
+import uuid
+import zlib
+from pathlib import Path
+
+try:
+    import zstandard
+    _COMPRESSOR = "zstd"
+except ImportError:
+    _COMPRESSOR = "zlib"
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+# -----------------------------
+# HYPERPARAMETERS
+# -----------------------------
+
+class Hyperparameters:
+    data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024")
+    train_files = os.path.join(data_path, "fineweb_train_*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_*.bin")
+    tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model")
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+    seed = int(os.environ.get("SEED", 42))
+
+    val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288))
+    val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500))
+    train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100))
+
+    iterations = int(os.environ.get("ITERATIONS", 20000))
+    warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000))
+    warmup_steps = int(os.environ.get("WARMUP_STEPS", 20))
+    train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432))
+    train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048))
+    max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0))
+    qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5))
+
+    vocab_size = int(os.environ.get("VOCAB_SIZE", 1024))
+    num_layers = int(os.environ.get("NUM_LAYERS", 11))
+    unique_layers = int(os.environ.get("UNIQUE_LAYERS", 10))
+    num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4))
+    model_dim = int(os.environ.get("MODEL_DIM", 512))
+    num_heads = int(os.environ.get("NUM_HEADS", 8))
+    mlp_mult = float(os.environ.get("MLP_MULT", 3.0))
+    mlp_activation = os.environ.get("MLP_ACTIVATION", "relu_sq")
+    tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1")))
+    rope_base = float(os.environ.get("ROPE_BASE", 10000.0))
+    logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0))
+
+    embed_lr = float(os.environ.get("EMBED_LR", 0.6))
+    head_lr = float(os.environ.get("HEAD_LR", 0.008))
+    tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03))
+    tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005))
+    matrix_lr = float(os.environ.get("MATRIX_LR", 0.02))
+    scalar_lr = float(os.environ.get("SCALAR_LR", 0.02))
+    muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99))
+    muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5))
+    muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500))
+    momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1")))
+    momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85))
+    momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95))
+    momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50))
+    beta1 = float(os.environ.get("BETA1", 0.9))
+    beta2 = float(os.environ.get("BETA2", 0.95))
+    adam_eps = float(os.environ.get("ADAM_EPS", 1e-8))
+    grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3))
+    weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04))
+
+    eval_stride = int(os.environ.get("EVAL_STRIDE", 64))
+    eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32))
+
+    bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240))
+    bigram_dim = int(os.environ.get("BIGRAM_DIM", 128))
+
+    swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1")))
+    swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4))
+    swa_every = int(os.environ.get("SWA_EVERY", 50))
+
+# -----------------------------
+# MUON OPTIMIZER
+# -----------------------------
+
+def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, weight_decay: float = 0.0):
+        super().__init__(
+            params,
+            dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, weight_decay=weight_decay),
+        )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr = group["lr"]
+            momentum = group["momentum"]
+            backend_steps = group["backend_steps"]
+            nesterov = group["nesterov"]
+
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr : curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+
+            wd = group.get("weight_decay", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.data.mul_(1.0 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+
+# -----------------------------
+# TOKENIZER-AGNOSTIC EVALUATION
+# -----------------------------
+
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}")
+    return tokens[: usable + 1]
+
+
+def eval_val(
+    args: Hyperparameters,
+    model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    grad_accum_steps: int,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+) -> tuple[float, float]:
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    if local_batch_tokens < args.train_seq_len:
+        raise ValueError(
+            "VAL_BATCH_SIZE must provide at least one sequence per rank; "
+            f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, "
+            f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}"
+        )
+    local_batch_seqs = local_batch_tokens // args.train_seq_len
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * args.train_seq_len
+            raw_end = batch_seq_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, args.train_seq_len)
+            y = local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+    val_loss = val_loss_sum / val_token_count
+    bits_per_token = val_loss.item() / math.log(2.0)
+    tokens_per_byte = val_token_count.item() / val_byte_count.item()
+    model.train()
+    return float(val_loss.item()), float(bits_per_token * tokens_per_byte)
+
+
+# -----------------------------
+# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed)
+# -----------------------------
+
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "CONTROL_TENSOR_NAME_PATTERNS",
+        "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,bigram.scale",
+    ).split(",")
+    if pattern
+)
+FP16_KEEP_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS",
+        ",".join(CONTROL_TENSOR_NAME_PATTERNS),
+    ).split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_MAX_NUMEL = 65_536
+INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_PERCENTILE = 99.99984
+INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0
+
+def tensor_nbytes(t: Tensor) -> int:
+    return int(t.numel()) * int(t.element_size())
+
+def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        clip_abs = (
+            torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1)
+            if t32.numel()
+            else torch.empty((t32.shape[0],), dtype=torch.float32)
+        )
+        clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None])
+        scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0)
+        q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous()
+        return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+    clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous()
+    return q, scale
+
+
+def _classify_param(name: str) -> str:
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if "bigram" in name:
+        return "bigram"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+
+def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        row_max = t32.abs().amax(dim=1)
+        scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16)
+        scale = scale.clamp_min(torch.finfo(torch.float16).tiny)
+        q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8)
+        return q, scale
+    amax = t32.abs().max().item()
+    scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16)
+    q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8)
+    return q, scale
+
+def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]):
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = _classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 8192:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS):
+            result[name] = t.to(dtype=torch.float16).contiguous()
+            meta[name] = "passthrough_fp16"
+            continue
+        if cat in int6_cats and t.ndim >= 1:
+            clip = 15 if cat == "mlp" else 31  # int5 for MLP, int6 for attention
+            q, s = quantize_intN_per_row(t, clip_range=clip)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    return result, meta
+
+def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object],
+                          template_sd: dict[str, Tensor]) -> dict[str, Tensor]:
+    out: dict[str, Tensor] = {}
+    for name, orig in template_sd.items():
+        info = meta[name]
+        orig_dtype = orig.dtype
+        if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"):
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + ".q"], result[name + ".scale"]
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+
+
+# -----------------------------
+# DATA LOADING
+# -----------------------------
+
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    expected_size = header_bytes + num_tokens * token_bytes
+    if file.stat().st_size != expected_size:
+        raise ValueError(f"Shard size mismatch for {file}: expected {expected_size} bytes")
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    if tokens_np.size != num_tokens:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = load_data_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self) -> None:
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = load_data_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks: list[Tensor] = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos : self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start : start + per_rank_span].to(dtype=torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+
+# -----------------------------
+# TRANSFORMER MODULES
+# -----------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+
+class CastedLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, None, :, :]
+            self._sin_cached = freqs.sin()[None, None, :, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        if dim % num_heads != 0:
+            raise ValueError("model_dim must be divisible by num_heads")
+        if num_heads % num_kv_heads != 0:
+            raise ValueError("num_heads must be divisible by num_kv_heads")
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        if self.head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin)
+        k = apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None]
+        y = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=None, is_causal=True,
+            enable_gqa=(self.num_kv_heads != self.num_heads),
+        )
+        y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, mlp_mult: float):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = torch.relu(self.fc(x))
+        return self.proj(x.square())
+
+
+class SmearGate(nn.Module):
+    """Blend each token's embedding with the previous token's embedding."""
+    def __init__(self, dim: int):
+        super().__init__()
+        self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :]
+        x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1)
+        return (1 - g) * x + g * x_prev
+
+
+class BigramHashEmbedding(nn.Module):
+    """Hash consecutive token pairs into a learned embedding table."""
+    def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int):
+        super().__init__()
+        self.bigram_vocab_size = bigram_vocab_size
+        self.embed = nn.Embedding(bigram_vocab_size, bigram_dim)
+        nn.init.zeros_(self.embed.weight)
+        self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32))
+
+    def bigram_hash(self, tokens: Tensor) -> Tensor:
+        t = tokens.to(torch.int32)
+        mod = self.bigram_vocab_size - 1
+        out = torch.empty_like(t)
+        out[..., 0] = mod
+        out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod
+        return out.long()
+
+    def forward(self, token_ids: Tensor) -> Tensor:
+        h = self.embed(self.bigram_hash(token_ids))
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x))
+        x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out
+        x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        num_layers: int,
+        model_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        mlp_mult: float,
+        tie_embeddings: bool,
+        tied_embed_init_std: float,
+        logit_softcap: float,
+        rope_base: float,
+        qk_gain_init: float,
+        bigram_vocab_size: int = 0,
+        bigram_dim: int = 128,
+        unique_layers: int = 0,
+    ):
+        super().__init__()
+        if logit_softcap <= 0.0:
+            raise ValueError(f"logit_softcap must be positive, got {logit_softcap}")
+        self.tie_embeddings = tie_embeddings
+        self.tied_embed_init_std = tied_embed_init_std
+        self.logit_softcap = logit_softcap
+        self.tok_emb = nn.Embedding(vocab_size, model_dim)
+        self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+        self.smear = SmearGate(model_dim)
+        # Layer sharing: create unique_layers physical blocks, map num_layers virtual layers to them
+        n_unique = unique_layers if unique_layers > 0 else num_layers
+        self.blocks = nn.ModuleList(
+            [
+                Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init)
+                for _ in range(n_unique)
+            ]
+        )
+        # Mapping from virtual layer index → physical block index
+        # Last virtual layer shares with the last physical block
+        self._layer_map = list(range(min(n_unique, num_layers)))
+        while len(self._layer_map) < num_layers:
+            self._layer_map.append(n_unique - 1)
+        self.final_norm = RMSNorm()
+        self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        num_layers = len(self._layer_map)  # virtual depth, not physical blocks
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, "_zero_init", False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+                    if ".proj." in name or name.endswith(".proj"):
+                        with torch.no_grad():
+                            module.weight.mul_(1.0 / math.sqrt(2 * num_layers))
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x).reshape(-1, x.size(-1))
+        targets = target_ids.reshape(-1)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            if self.lm_head is None:
+                raise RuntimeError("lm_head is required when tie_embeddings=False")
+            logits_proj = self.lm_head(x)
+        logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+        return F.cross_entropy(logits.float(), targets, reduction="mean")
+
+    def forward_logits(self, input_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+
+
+def eval_val_sliding(
+    args: Hyperparameters,
+    base_model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+    stride: int,
+    batch_seqs: int = 32,
+) -> tuple[float, float]:
+    seq_len = args.train_seq_len
+    total_tokens = val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, stride)
+                     if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0]
+    total_windows = len(window_starts)
+    my_s = (total_windows * rank) // world_size
+    my_e = (total_windows * (rank + 1)) // world_size
+    my_windows = window_starts[my_s:my_e]
+
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    base_model.eval()
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens: list[int] = []
+            for i, ws in enumerate(batch_ws):
+                end = min(ws + seq_len, total_tokens)
+                wlen = end - ws
+                wlens.append(wlen)
+                chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits = base_model.forward_logits(x_batch)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else max(wlen - stride, 0)
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = base_bytes_lut[tgt].to(torch.float64)
+                tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+            if rank == 0 and (bi // batch_seqs) % 50 == 0:
+                done = min(bi + batch_seqs, len(my_windows))
+                pct = done / len(my_windows) * 100
+                running_bpb = 0.0
+                if token_count.item() > 0:
+                    rl = (loss_sum / token_count).item()
+                    running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item())
+                print(f"  sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True)
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    val_loss = (loss_sum / token_count).item()
+    bits_per_token = val_loss / math.log(2.0)
+    tokens_per_byte = token_count.item() / byte_count.item()
+    base_model.train()
+    return val_loss, bits_per_token * tokens_per_byte
+
+
+# -----------------------------
+# CO-OCCURRENCE QK INIT
+# -----------------------------
+
+def build_cooccurrence_matrix(data_pattern: str, vocab_size: int, max_tokens: int = 2_000_000) -> Tensor:
+    """Build a vocab×vocab bigram co-occurrence matrix from training data. Fast: ~1s for 2M tokens."""
+    stream = TokenStream(data_pattern)
+    tokens = stream.take(min(max_tokens, stream.tokens.numel()))
+    tokens_np = tokens.numpy().astype(np.int64)
+    C = np.zeros((vocab_size, vocab_size), dtype=np.float64)
+    for i in range(len(tokens_np) - 1):
+        C[tokens_np[i], tokens_np[i + 1]] += 1.0
+    # Symmetrize: both (i,j) and (j,i) indicate co-occurrence
+    C = C + C.T
+    # Log-PMI-like transform: log(C + 1) then center rows and columns
+    C = np.log1p(C)
+    C -= C.mean(axis=1, keepdims=True)
+    C -= C.mean(axis=0, keepdims=True)
+    return torch.from_numpy(C).float()
+
+
+def init_qk_from_cooccurrence(model: nn.Module, cooc: Tensor, num_heads: int, num_kv_heads: int, head_dim: int) -> None:
+    """Initialize W_Q and W_K in layer 0 so that Q·K^T approximates the co-occurrence structure.
+    SVD the co-occurrence matrix, distribute top components across heads."""
+    U, S, Vt = torch.linalg.svd(cooc, full_matrices=False)
+    # We need num_heads * head_dim components for Q, num_kv_heads * head_dim for K
+    q_dim = num_heads * head_dim
+    k_dim = num_kv_heads * head_dim
+    n_components = max(q_dim, k_dim)
+    # Scale by sqrt(S) so that Q·K^T ≈ U·S·V^T
+    S_sqrt = S[:n_components].sqrt()
+    Q_init = U[:, :q_dim] * S_sqrt[:q_dim].unsqueeze(0)  # [vocab, q_dim]
+    K_init = Vt[:k_dim, :].T * S_sqrt[:k_dim].unsqueeze(0)  # [vocab, k_dim]
+    # But W_Q maps from model_dim (=embedding dim at layer 0) to q_dim
+    # At init, hidden states ≈ embeddings. We need W_Q such that E @ W_Q ≈ Q_init
+    # Since E is ~orthogonal at init, W_Q ≈ E^T @ Q_init (pseudo-inverse)
+    # However, E is vocab×dim, not square. Use E's pseudoinverse.
+    # Simpler approach: directly set W_Q weight rows from SVD components
+    # W_Q: [q_dim, model_dim]. At step 0: q = x @ W_Q^T = E[tok] @ W_Q^T
+    # We want the dot product q_i · k_j to reflect co-occurrence.
+    # Set W_Q = Q_init^T (projected to model_dim) — but Q_init is [vocab, q_dim]
+    # This doesn't directly work because W_Q operates on dim, not vocab.
+    #
+    # Correct approach: W_Q and W_K are [out_dim, model_dim].
+    # q = h @ W_Q^T where h is the hidden state (≈ embedding at layer 0)
+    # We want: (h_i @ W_Q^T) @ (W_K @ h_j^T) = h_i^T @ W_Q^T @ W_K @ h_j ≈ C[tok_i, tok_j]
+    # So W_Q^T @ W_K ≈ E_pinv @ C @ E_pinv^T where E_pinv is pseudoinverse of E
+    # But E is not available at model construction time (it's a parameter).
+    # Instead, use a simpler signal: set W_Q and W_K from SVD of C projected through
+    # a random projection (since E is random at init).
+    # Actually simplest: just scale the existing orthogonal init by the SVD singular values
+    # to bias different heads toward different co-occurrence patterns.
+
+    # Practical approach: for layer 0, replace W_Q and W_K with SVD-derived weights
+    # We project the SVD components into model_dim space via random projection
+    block0 = list(model.blocks.children())[0] if hasattr(model, 'blocks') else None
+    if block0 is None:
+        return
+    model_dim = block0.attn.c_q.weight.shape[1]
+    # Random projection from vocab_size to model_dim (fixed seed for reproducibility)
+    rng = torch.Generator()
+    rng.manual_seed(12345)
+    P = torch.randn(cooc.shape[0], model_dim, generator=rng) / math.sqrt(model_dim)
+    # Project co-occurrence into model_dim space: C_proj = P^T @ C @ P [model_dim, model_dim]
+    C_proj = P.T @ cooc @ P
+    # SVD of projected matrix
+    U2, S2, Vt2 = torch.linalg.svd(C_proj, full_matrices=False)
+    S2_sqrt = S2[:n_components].clamp_min(0).sqrt()
+    # W_Q rows = U2 columns scaled by sqrt(S), reshaped to [q_dim, model_dim]
+    # W_K rows = V2 columns scaled by sqrt(S), reshaped to [k_dim, model_dim]
+    with torch.no_grad():
+        q_weight = (U2[:, :q_dim] * S2_sqrt[:q_dim].unsqueeze(0)).T  # [q_dim, model_dim]
+        k_weight = (Vt2[:k_dim, :].T * S2_sqrt[:k_dim].unsqueeze(0)).T  # [k_dim, model_dim]
+        # Normalize to match the scale of orthogonal init
+        q_scale = block0.attn.c_q.weight.data.norm() / q_weight.norm().clamp_min(1e-8)
+        k_scale = block0.attn.c_k.weight.data.norm() / k_weight.norm().clamp_min(1e-8)
+        block0.attn.c_q.weight.data.copy_(q_weight.to(device=block0.attn.c_q.weight.device, dtype=block0.attn.c_q.weight.dtype) * q_scale)
+        block0.attn.c_k.weight.data.copy_(k_weight.to(device=block0.attn.c_k.weight.device, dtype=block0.attn.c_k.weight.dtype) * k_scale)
+
+
+# -----------------------------
+# TRAINING
+# -----------------------------
+
+def main() -> None:
+    global zeropower_via_newtonschulz5
+
+    code = Path(__file__).read_text(encoding="utf-8")
+    args = Hyperparameters()
+    zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral")
+    grad_accum_steps = 8 // world_size
+    grad_scale = 1.0 / grad_accum_steps
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+
+    logfile = None
+    if master_process:
+        os.makedirs("logs", exist_ok=True)
+        logfile = f"logs/{args.run_id}.txt"
+        print(logfile)
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile is not None:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+    log0(f"Running Python {sys.version}", console=False)
+    log0(f"Running PyTorch {torch.__version__}", console=False)
+    log0(
+        subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout,
+        console=False,
+    )
+    log0("=" * 100, console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    if not args.tokenizer_path.endswith(".model"):
+        raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}")
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    if int(sp.vocab_size()) != args.vocab_size:
+        raise ValueError(
+            f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}"
+        )
+    dataset_dir = Path(args.data_path).resolve()
+    actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin")))
+    val_tokens = load_validation_tokens(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(
+        sp, args.vocab_size, device
+    )
+    log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}")
+    log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}")
+    log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}")
+
+    # MODEL + OPTIMIZER SETUP
+    base_model = GPT(
+        vocab_size=args.vocab_size,
+        num_layers=args.num_layers,
+        model_dim=args.model_dim,
+        num_heads=args.num_heads,
+        num_kv_heads=args.num_kv_heads,
+        mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings,
+        tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap,
+        rope_base=args.rope_base,
+        qk_gain_init=args.qk_gain_init,
+        bigram_vocab_size=args.bigram_vocab_size,
+        bigram_dim=args.bigram_dim,
+        unique_layers=args.unique_layers,
+    ).to(device).bfloat16()
+    for module in base_model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+
+    # Co-occurrence QK init for layer 0
+    cooc_init_enabled = bool(int(os.environ.get("COOC_QK_INIT", "1")))
+    if cooc_init_enabled and master_process:
+        log0("cooc_qk_init: building co-occurrence matrix from training data...")
+    if cooc_init_enabled:
+        cooc_matrix = build_cooccurrence_matrix(args.train_files, args.vocab_size, max_tokens=2_000_000)
+        init_qk_from_cooccurrence(
+            base_model, cooc_matrix,
+            num_heads=args.num_heads, num_kv_heads=args.num_kv_heads,
+            head_dim=args.model_dim // args.num_heads,
+        )
+        if master_process:
+            log0("cooc_qk_init: done, W_Q and W_K in layer 0 initialized from co-occurrence SVD")
+
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model
+
+    block_named_params = list(base_model.blocks.named_parameters())
+    matrix_params = [
+        p for name, p in block_named_params
+        if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    scalar_params = [
+        p for name, p in block_named_params
+        if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    if base_model.skip_weights.numel() > 0:
+        scalar_params.append(base_model.skip_weights)
+    scalar_params.append(base_model.smear.gate)
+    if base_model.bigram is not None:
+        scalar_params.append(base_model.bigram.scale)
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}]
+    if base_model.bigram is not None:
+        tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr})
+        if base_model.bigram.proj is not None:
+            matrix_params.append(base_model.bigram.proj.weight)
+
+    optimizer_tok = torch.optim.AdamW(
+        tok_params,
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizer_muon = Muon(
+        matrix_params,
+        lr=args.matrix_lr,
+        momentum=args.muon_momentum,
+        backend_steps=args.muon_backend_steps,
+        weight_decay=0.04,
+    )
+    for group in optimizer_muon.param_groups:
+        group["base_lr"] = args.matrix_lr
+    optimizer_scalar = torch.optim.AdamW(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar]
+    if base_model.lm_head is not None:
+        optimizer_head = torch.optim.Adam(
+            [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}],
+            betas=(args.beta1, args.beta2),
+            eps=args.adam_eps,
+            fused=True,
+        )
+        optimizers.insert(1, optimizer_head)
+
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"model_params:{n_params}")
+    log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}")
+    log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}")
+    log0(
+        f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} "
+        f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}"
+    )
+    log0(
+        f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} "
+        f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} "
+        f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}"
+    )
+    log0(f"seed:{args.seed}")
+
+    # DATA LOADER & MODEL WARMUP
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all() -> None:
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float) -> float:
+        if args.warmdown_iters <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = max(args.iterations - args.warmdown_iters, 0)
+            return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0
+        step_ms = elapsed_ms / max(step, 1)
+        warmdown_ms = args.warmdown_iters * step_ms
+        remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+        return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+
+    if args.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(args.warmup_steps):
+            zero_grad_all()
+            for micro_step in range(grad_accum_steps):
+                if distributed:
+                    model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+                x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                    warmup_loss = model(x, y)
+                (warmup_loss * grad_scale).backward()
+            for opt in optimizers:
+                opt.step()
+            zero_grad_all()
+            if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps:
+                log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}")
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        zero_grad_all()
+        if distributed:
+            model.require_backward_grad_sync = True
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # MAIN TRAINING LOOP
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    swa_state: dict[str, Tensor] | None = None
+    swa_count = 0
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    step = 0
+    while True:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            )
+            log0(
+                f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms"
+            )
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(
+                    f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms "
+                    f"step:{step}/{args.iterations}"
+                )
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+        zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+        for micro_step in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+            x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        if step < args.muon_momentum_warmup_steps:
+            # Linear warmup phase
+            frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0
+            muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+        elif args.momentum_cyclic:
+            # Cyclic momentum: triangle wave between momentum_min and momentum_max
+            period = args.momentum_cycle_period * 2
+            pos = (step % period) / period
+            if pos < 0.5:
+                muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2)
+            else:
+                muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2)
+        else:
+            muon_momentum = args.muon_momentum
+        for group in optimizer_muon.param_groups:
+            group["momentum"] = muon_momentum
+
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group["lr"] = group["base_lr"] * scale
+
+        if args.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm)
+        for opt in optimizers:
+            opt.step()
+        zero_grad_all()
+
+        step += 1
+        approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+
+        # SWA: collect checkpoints during warmdown
+        if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0:
+            if swa_state is None:
+                swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}
+                swa_count = 1
+                log0(f"swa:start step:{step}")
+            else:
+                for name, t in base_model.state_dict().items():
+                    swa_state[name] += t.detach().cpu()
+                swa_count += 1
+
+        should_log_train = (
+            args.train_log_every > 0
+            and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None)
+        )
+        if should_log_train:
+            log0(
+                f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} "
+                f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms"
+            )
+
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log0(
+        f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+
+    # Apply SWA if collected
+    if args.swa_enabled and swa_state is not None and swa_count > 1:
+        log0(f"swa:applying averaged {swa_count} checkpoints")
+        current_state = base_model.state_dict()
+        avg_state = {
+            name: (tensor / swa_count).to(dtype=current_state[name].dtype)
+            for name, tensor in swa_state.items()
+        }
+        base_model.load_state_dict(avg_state, strict=True)
+
+    # SERIALIZATION + ROUNDTRIP VALIDATION
+    if master_process:
+        torch.save(base_model.state_dict(), "final_model.pt")
+        model_bytes = os.path.getsize("final_model.pt")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model: {model_bytes} bytes")
+        log0(f"Code size: {code_bytes} bytes")
+        log0(f"Total submission size: {model_bytes + code_bytes} bytes")
+
+    # Magnitude pruning: zero out smallest weights to improve compression
+    with torch.no_grad():
+        for name, param in base_model.named_parameters():
+            if param.ndim == 2 and param.numel() > 65536:
+                threshold = torch.quantile(param.abs().float().flatten(), 0.03)
+                mask = param.abs() < threshold
+                param.masked_fill_(mask, 0.0)
+
+    # AWQ: Activation-aware weight scaling before quantization
+    awq_alpha = float(os.environ.get("AWQ_ALPHA", 0.5))
+    awq_enabled = bool(int(os.environ.get("AWQ_ENABLED", "1")))
+    if awq_enabled:
+        log0(f"awq:calibrating alpha={awq_alpha}")
+        # Step 1: Collect per-channel activation magnitudes via hooks
+        act_stats: dict[str, Tensor] = {}
+        hooks = []
+        def make_hook(name):
+            def hook_fn(module, input, output):
+                x = input[0] if isinstance(input, tuple) else input
+                if x.ndim >= 2:
+                    # Mean absolute activation per channel (last dim)
+                    s = x.detach().float().abs().mean(dim=tuple(range(x.ndim - 1)))
+                    if name in act_stats:
+                        act_stats[name] = act_stats[name] + s
+                    else:
+                        act_stats[name] = s
+            return hook_fn
+
+        for name, module in base_model.named_modules():
+            if isinstance(module, (nn.Linear, CastedLinear)):
+                hooks.append(module.register_forward_hook(make_hook(name)))
+
+        # Step 2: Run calibration batches (use val data, 4 batches)
+        base_model.eval()
+        n_calib_batches = 4
+        calib_seq_len = args.train_seq_len
+        calib_batch_seqs = 16
+        calib_tokens_per_batch = calib_batch_seqs * calib_seq_len
+        with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            for cb in range(n_calib_batches):
+                start = cb * calib_tokens_per_batch
+                end = start + calib_tokens_per_batch + 1
+                if end > val_tokens.numel():
+                    break
+                local = val_tokens[start:end].to(device=device, dtype=torch.int64)
+                x = local[:-1].reshape(-1, calib_seq_len)
+                base_model.forward_logits(x)
+
+        for h in hooks:
+            h.remove()
+
+        # Normalize activation stats
+        for name in act_stats:
+            act_stats[name] = act_stats[name] / n_calib_batches
+
+        # Step 3: Scale weight columns by s^alpha
+        awq_scales = {}
+        with torch.no_grad():
+            for name, module in base_model.named_modules():
+                if isinstance(module, (nn.Linear, CastedLinear)) and name in act_stats:
+                    s = act_stats[name].to(module.weight.device)
+                    s = s.clamp_min(1e-6)
+                    scale = s.pow(awq_alpha)
+                    # Scale weight columns (input channels)
+                    if module.weight.shape[1] == scale.shape[0]:
+                        module.weight.data = module.weight.data * scale.unsqueeze(0)
+                        awq_scales[name] = scale.cpu()
+                        # Store inverse scale to apply to inputs at inference
+                        # We'll fold this into the state dict
+
+        log0(f"awq:scaled {len(awq_scales)} layers")
+
+    # INT6 mixed quantization + zstd/zlib export
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()}
+    # Store AWQ inverse scales in the state dict for inference compensation
+    if awq_enabled and awq_scales:
+        for lname, scale in awq_scales.items():
+            sd_cpu[f"_awq_inv_scale.{lname}"] = (1.0 / scale).to(torch.float16)
+    quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"})
+    quant_buf = io.BytesIO()
+    torch.save({"w": quant_result, "m": quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    if _COMPRESSOR == "zstd":
+        quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw)
+    else:
+        quant_blob = zlib.compress(quant_raw, 9)
+    if master_process:
+        with open("final_model.int8.ptz", "wb") as f:
+            f.write(quant_blob)
+        quant_file_bytes = os.path.getsize("final_model.int8.ptz")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes")
+        log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes")
+
+    if distributed:
+        dist.barrier()
+    with open("final_model.int8.ptz", "rb") as f:
+        quant_blob_disk = f.read()
+    if _COMPRESSOR == "zstd":
+        decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk)
+    else:
+        decompressed = zlib.decompress(quant_blob_disk)
+    quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu")
+    deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu)
+    # AWQ: undo column scaling after dequantization
+    if awq_enabled:
+        awq_inv_keys = [k for k in deq_state if k.startswith("_awq_inv_scale.")]
+        for inv_key in awq_inv_keys:
+            inv_scale = deq_state.pop(inv_key).float()
+            layer_name = inv_key.replace("_awq_inv_scale.", "")
+            weight_key = layer_name + ".weight"
+            if weight_key in deq_state:
+                # Undo: W_orig = W_scaled * inv_scale (per column)
+                deq_state[weight_key] = deq_state[weight_key].float() * inv_scale.unsqueeze(0)
+                deq_state[weight_key] = deq_state[weight_key].to(dtype=sd_cpu[weight_key].dtype)
+        log0(f"awq:unscaled {len(awq_inv_keys)} layers after dequant")
+    # Remove any remaining AWQ keys before loading
+    deq_state = {k: v for k, v in deq_state.items() if not k.startswith("_awq_")}
+    base_model.load_state_dict(deq_state, strict=True)
+
+    # Sliding window eval on int6-roundtripped weights
+    torch.cuda.synchronize()
+    t_qeval = time.perf_counter()
+    if args.eval_stride > 0 and args.eval_stride < args.train_seq_len:
+        log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}")
+        q_val_loss, q_val_bpb = eval_val_sliding(
+            args, base_model, rank, world_size, device,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            stride=args.eval_stride, batch_seqs=args.eval_batch_seqs,
+        )
+    else:
+        log0("final_eval_mode:standard")
+        q_val_loss, q_val_bpb = eval_val(
+            args, model, rank, world_size, device, grad_accum_steps,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+        )
+    torch.cuda.synchronize()
+    log0(
+        f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+        f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms"
+    )
+    log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
+# fixes applied
+# tuned
+
+====================================================================================================
+Running Python 3.12.3 (main, Nov  6 2025, 13:44:16) [GCC 13.3.0]
+Running PyTorch 2.9.1+cu128
+Tue Mar 24 18:22:08 2026       
++-----------------------------------------------------------------------------------------+
+| NVIDIA-SMI 580.126.09             Driver Version: 580.126.09     CUDA Version: 13.0     |
++-----------------------------------------+------------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
+|                                         |                        |               MIG M. |
+|=========================================+========================+======================|
+|   0  NVIDIA H100 80GB HBM3          On  |   00000000:9A:00.0 Off |                    0 |
+| N/A   28C    P0             86W /  700W |     527MiB /  81559MiB |      0%      Default |
+|                                         |                        |             Disabled |
++-----------------------------------------+------------------------+----------------------+
+
++-----------------------------------------------------------------------------------------+
+| Processes:                                                                              |
+|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
+|        ID   ID                                                               Usage      |
+|=========================================================================================|
+|    0   N/A  N/A           21068      C   python3                                 518MiB |
++-----------------------------------------------------------------------------------------+
+
+====================================================================================================
+val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path=./data/tokenizers/fineweb_1024_bpe.model
+train_loader:dataset:fineweb10B_sp1024 train_shards:80
+val_loader:shards pattern=./data/datasets/fineweb10B_sp1024/fineweb_val_*.bin tokens:62021632
+cooc_qk_init: building co-occurrence matrix from training data...
+cooc_qk_init: done, W_Q and W_K in layer 0 initialized from co-occurrence SVD
+model_params:25517137
+world_size:1 grad_accum_steps:8
+attention_mode:gqa num_heads:8 num_kv_heads:4
+tie_embeddings:True embed_lr:0.035 matrix_lr:0.025 scalar_lr:0.025
+train_batch_tokens:524288 train_seq_len:2048 iterations:20000 warmup_steps:20 max_wallclock_seconds:600.000
+seed:42
+warmup_step:1/20
+warmup_step:2/20
+warmup_step:3/20
+warmup_step:4/20
+warmup_step:5/20
+warmup_step:6/20
+warmup_step:7/20
+warmup_step:8/20
+warmup_step:9/20
+warmup_step:10/20
+warmup_step:11/20
+warmup_step:12/20
+warmup_step:13/20
+warmup_step:14/20
+warmup_step:15/20
+warmup_step:16/20
+warmup_step:17/20
+warmup_step:18/20
+warmup_step:19/20
+warmup_step:20/20
+step:0/20000 val_loss:6.9323 val_bpb:4.1057 train_time:0ms step_avg:0.02ms
+step:1/20000 train_loss:6.9335 train_time:548ms step_avg:547.60ms
+step:2/20000 train_loss:8.8478 train_time:1101ms step_avg:550.55ms
+step:3/20000 train_loss:8.4486 train_time:1632ms step_avg:543.84ms
+step:4/20000 train_loss:7.7646 train_time:2198ms step_avg:549.51ms
+step:5/20000 train_loss:7.1397 train_time:2740ms step_avg:547.97ms
+step:6/20000 train_loss:6.7020 train_time:3293ms step_avg:548.79ms
+step:7/20000 train_loss:6.2936 train_time:3844ms step_avg:549.21ms
+step:8/20000 train_loss:6.1048 train_time:4399ms step_avg:549.83ms
+step:9/20000 train_loss:5.9824 train_time:4944ms step_avg:549.28ms
+step:10/20000 train_loss:5.8981 train_time:5479ms step_avg:547.89ms
+step:25/20000 train_loss:5.1677 train_time:13467ms step_avg:538.66ms
+step:50/20000 train_loss:3.8390 train_time:26718ms step_avg:534.35ms
+step:75/20000 train_loss:3.4511 train_time:40014ms step_avg:533.52ms
+step:100/20000 train_loss:3.2106 train_time:53026ms step_avg:530.26ms
+step:100/20000 val_loss:3.2205 val_bpb:1.9073 train_time:53043ms step_avg:530.43ms
+step:125/20000 train_loss:3.0733 train_time:66134ms step_avg:529.07ms
+step:150/20000 train_loss:2.9838 train_time:79308ms step_avg:528.72ms
+step:175/20000 train_loss:2.8149 train_time:92288ms step_avg:527.36ms
+step:200/20000 train_loss:2.7380 train_time:106550ms step_avg:532.75ms
+step:200/20000 val_loss:2.7826 val_bpb:1.6480 train_time:106574ms step_avg:532.87ms
+step:225/20000 train_loss:2.7320 train_time:119604ms step_avg:531.57ms
+step:250/20000 train_loss:2.6843 train_time:132736ms step_avg:530.95ms
+step:275/20000 train_loss:2.5843 train_time:145991ms step_avg:530.88ms
+step:300/20000 train_loss:2.5525 train_time:159423ms step_avg:531.41ms
+step:300/20000 val_loss:2.5822 val_bpb:1.5293 train_time:159451ms step_avg:531.50ms
+step:325/20000 train_loss:2.5580 train_time:172610ms step_avg:531.11ms
+step:350/20000 train_loss:2.5409 train_time:186013ms step_avg:531.46ms
+step:375/20000 train_loss:2.5416 train_time:199583ms step_avg:532.22ms
+step:400/20000 train_loss:2.3160 train_time:218680ms step_avg:546.70ms
+step:400/20000 val_loss:2.4794 val_bpb:1.4685 train_time:218716ms step_avg:546.79ms
+step:425/20000 train_loss:2.4497 train_time:232102ms step_avg:546.12ms
+step:450/20000 train_loss:2.4266 train_time:246170ms step_avg:547.04ms
+step:475/20000 train_loss:2.4498 train_time:259795ms step_avg:546.94ms
+swa:start step:500
+step:500/20000 train_loss:2.4200 train_time:273698ms step_avg:547.40ms
+step:500/20000 val_loss:2.4112 val_bpb:1.4281 train_time:274052ms step_avg:548.10ms
+step:525/20000 train_loss:2.4750 train_time:287610ms step_avg:547.83ms
+step:550/20000 train_loss:2.2941 train_time:301497ms step_avg:548.18ms
+step:575/20000 train_loss:2.3513 train_time:316217ms step_avg:549.94ms
+step:600/20000 train_loss:2.4148 train_time:329542ms step_avg:549.24ms
+step:600/20000 val_loss:2.3653 val_bpb:1.4008 train_time:329773ms step_avg:549.62ms
+step:625/20000 train_loss:2.4218 train_time:343007ms step_avg:548.81ms
+step:650/20000 train_loss:2.2302 train_time:356389ms step_avg:548.29ms
+step:675/20000 train_loss:2.2239 train_time:370042ms step_avg:548.21ms
+step:700/20000 train_loss:2.3745 train_time:383937ms step_avg:548.48ms
+step:700/20000 val_loss:2.3266 val_bpb:1.3780 train_time:384341ms step_avg:549.06ms
+step:725/20000 train_loss:2.3738 train_time:397694ms step_avg:548.54ms
+step:750/20000 train_loss:2.2705 train_time:411295ms step_avg:548.39ms
+step:775/20000 train_loss:2.3580 train_time:426595ms step_avg:550.44ms
+step:800/20000 train_loss:2.2639 train_time:439822ms step_avg:549.78ms
+step:800/20000 val_loss:2.2967 val_bpb:1.3603 train_time:440124ms step_avg:550.16ms
+step:825/20000 train_loss:2.3659 train_time:453451ms step_avg:549.64ms
+step:850/20000 train_loss:2.3646 train_time:466828ms step_avg:549.21ms
+step:875/20000 train_loss:2.2751 train_time:480541ms step_avg:549.19ms
+step:900/20000 train_loss:2.2894 train_time:494273ms step_avg:549.19ms
+step:900/20000 val_loss:2.2713 val_bpb:1.3452 train_time:494381ms step_avg:549.31ms
+step:925/20000 train_loss:2.2073 train_time:507739ms step_avg:548.91ms
+step:950/20000 train_loss:2.2970 train_time:520999ms step_avg:548.42ms
+step:975/20000 train_loss:2.2168 train_time:534601ms step_avg:548.31ms
+step:1000/20000 train_loss:2.2837 train_time:547685ms step_avg:547.68ms
+step:1000/20000 val_loss:2.2493 val_bpb:1.3322 train_time:547893ms step_avg:547.89ms
+step:1025/20000 train_loss:2.3489 train_time:561110ms step_avg:547.42ms
+step:1050/20000 train_loss:2.3059 train_time:574396ms step_avg:547.04ms
+step:1075/20000 train_loss:2.1857 train_time:587500ms step_avg:546.51ms
+step:1099/20000 val_loss:2.2364 val_bpb:1.3245 train_time:600138ms step_avg:546.08ms
+stopping_early: wallclock_cap train_time:600138ms step:1099/20000
+peak memory allocated: 14656 MiB reserved: 14872 MiB
+swa:applying averaged 12 checkpoints
+Serialized model: 98437419 bytes
+Code size: 64061 bytes
+Total submission size: 98501480 bytes
+awq:calibrating alpha=0.5
+awq:scaled 61 layers
+Serialized model int6+zstd: 15481926 bytes
+Total submission size int8+zlib: 15545987 bytes
+awq:unscaled 61 layers after dequant
+final_eval_mode:standard
+final_int8_zlib_roundtrip val_loss:2.2837 val_bpb:1.3525 eval_time:18099ms
+final_int8_zlib_roundtrip_exact val_loss:2.28368322 val_bpb:1.35252584
diff --git a/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/run.sh b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/run.sh
new file mode 100755
index 000000000..c07b68d98
--- /dev/null
+++ b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/run.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+# ============================================================
+# Exp29: Co-occurrence QK Init — from Exp18
+# Builds bigram co-occurrence matrix from first 2M training tokens (~1s),
+# SVDs it, initializes W_Q and W_K in layer 0 so initial attention
+# patterns reflect token co-occurrence structure. Zero extra params.
+# ============================================================
+set -euo pipefail
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+EXP_NAME="exp29_cooccurrence-qk-init_from-exp18"
+cd /workspace/parameter-golf
+export EVAL_STRIDE=0
+export SEED="${SEED:-42}"
+export ITERATIONS="${ITERATIONS:-20000}"
+export WARMUP_STEPS="${WARMUP_STEPS:-20}"
+export TRAIN_BATCH_TOKENS="${TRAIN_BATCH_TOKENS:-524288}"
+export TRAIN_SEQ_LEN="${TRAIN_SEQ_LEN:-2048}"
+export VAL_LOSS_EVERY="${VAL_LOSS_EVERY:-100}"
+export VAL_BATCH_SIZE="${VAL_BATCH_SIZE:-262144}"
+export TRAIN_LOG_EVERY="${TRAIN_LOG_EVERY:-25}"
+export MAX_WALLCLOCK_SECONDS="${MAX_WALLCLOCK_SECONDS:-600}"
+export NUM_LAYERS=11
+export UNIQUE_LAYERS=10
+export MLP_ACTIVATION=relu_sq
+export MATRIX_LR=0.025
+export SCALAR_LR=0.025
+export TIED_EMBED_LR=0.035
+export SWA_START_FRAC=0.2
+export SWA_EVERY=50
+export MOMENTUM_CYCLIC=1
+export MOMENTUM_MIN=0.85
+export MOMENTUM_MAX=0.95
+export MOMENTUM_CYCLE_PERIOD=50
+export AWQ_ENABLED=1
+export AWQ_ALPHA=0.5
+export COOC_QK_INIT=1
+export RUN_ID="${EXP_NAME}"
+echo "=== ${EXP_NAME} ==="
+echo "Co-occurrence QK init: layer 0, 2M tokens"
+python3 "${SCRIPT_DIR}/train_gpt.py" 2>&1 | tee "${SCRIPT_DIR}/logs.txt"
+echo "=== ${EXP_NAME} COMPLETE ==="
diff --git a/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/submission.json b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/submission.json
new file mode 100644
index 000000000..da3ea0fdd
--- /dev/null
+++ b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/submission.json
@@ -0,0 +1,17 @@
+{
+  "author": "Sidhant Thole",
+  "github_id": "SPThole",
+  "name": "Co-occurrence QK Initialization",
+  "blurb": "Initializes W_Q and W_K in layer 0 from bigram co-occurrence SVD, giving attention a distributional head start. Scans 2M tokens at startup (<3s), applies SVD, projects into model space. Zero extra parameters. Built on exp18 baseline. Run on 1×H100. Based on PR #623.",
+  "date": "2026-03-24T19:15:00Z",
+  "val_loss": 2.28368322,
+  "val_bpb": 1.35252584,
+  "pre_quant_val_loss": 2.2364,
+  "pre_quant_val_bpb": 1.3245,
+  "step_stop": 1099,
+  "wallclock_seconds": 600.138,
+  "bytes_total": 15545987,
+  "bytes_code": 64061,
+  "gpu_config": "1×H100 80GB",
+  "base_pr": "https://github.com/openai/parameter-golf/pull/623"
+}
diff --git a/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/train_gpt.py b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/train_gpt.py
new file mode 100644
index 000000000..d60a73c02
--- /dev/null
+++ b/records/track_non_record_16mb/2026_03_24_cooccurrence-qk-init_from-exp18/train_gpt.py
@@ -0,0 +1,1435 @@
+"""
+The `train_gpt.py` and `train_gpt_mlx.py` scripts are intended as good launching-off points for new participants, not SOTA configs. We'll accept PRs that tune, improve, or simplify these scripts without significantly increasing complexity, but competitive submissions should stay in the `/records` folder.
+
+Hard stop: To keep readable for newcomers, let's make sure `train_gpt.py` and `train_gpt_mlx.py` never are longer than 1500 lines.
+"""
+
+from __future__ import annotations
+
+import copy
+import glob
+import io
+import math
+import os
+import random
+import subprocess
+import sys
+import time
+import uuid
+import zlib
+from pathlib import Path
+
+try:
+    import zstandard
+    _COMPRESSOR = "zstd"
+except ImportError:
+    _COMPRESSOR = "zlib"
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch import Tensor, nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+# -----------------------------
+# HYPERPARAMETERS
+# -----------------------------
+
+class Hyperparameters:
+    data_path = os.environ.get("DATA_PATH", "./data/datasets/fineweb10B_sp1024")
+    train_files = os.path.join(data_path, "fineweb_train_*.bin")
+    val_files = os.path.join(data_path, "fineweb_val_*.bin")
+    tokenizer_path = os.environ.get("TOKENIZER_PATH", "./data/tokenizers/fineweb_1024_bpe.model")
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+    seed = int(os.environ.get("SEED", 42))
+
+    val_batch_size = int(os.environ.get("VAL_BATCH_SIZE", 524_288))
+    val_loss_every = int(os.environ.get("VAL_LOSS_EVERY", 500))
+    train_log_every = int(os.environ.get("TRAIN_LOG_EVERY", 100))
+
+    iterations = int(os.environ.get("ITERATIONS", 20000))
+    warmdown_iters = int(os.environ.get("WARMDOWN_ITERS", 3000))
+    warmup_steps = int(os.environ.get("WARMUP_STEPS", 20))
+    train_batch_tokens = int(os.environ.get("TRAIN_BATCH_TOKENS", 786_432))
+    train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", 2048))
+    max_wallclock_seconds = float(os.environ.get("MAX_WALLCLOCK_SECONDS", 600.0))
+    qk_gain_init = float(os.environ.get("QK_GAIN_INIT", 1.5))
+
+    vocab_size = int(os.environ.get("VOCAB_SIZE", 1024))
+    num_layers = int(os.environ.get("NUM_LAYERS", 11))
+    unique_layers = int(os.environ.get("UNIQUE_LAYERS", 10))
+    num_kv_heads = int(os.environ.get("NUM_KV_HEADS", 4))
+    model_dim = int(os.environ.get("MODEL_DIM", 512))
+    num_heads = int(os.environ.get("NUM_HEADS", 8))
+    mlp_mult = float(os.environ.get("MLP_MULT", 3.0))
+    mlp_activation = os.environ.get("MLP_ACTIVATION", "relu_sq")
+    tie_embeddings = bool(int(os.environ.get("TIE_EMBEDDINGS", "1")))
+    rope_base = float(os.environ.get("ROPE_BASE", 10000.0))
+    logit_softcap = float(os.environ.get("LOGIT_SOFTCAP", 30.0))
+
+    embed_lr = float(os.environ.get("EMBED_LR", 0.6))
+    head_lr = float(os.environ.get("HEAD_LR", 0.008))
+    tied_embed_lr = float(os.environ.get("TIED_EMBED_LR", 0.03))
+    tied_embed_init_std = float(os.environ.get("TIED_EMBED_INIT_STD", 0.005))
+    matrix_lr = float(os.environ.get("MATRIX_LR", 0.02))
+    scalar_lr = float(os.environ.get("SCALAR_LR", 0.02))
+    muon_momentum = float(os.environ.get("MUON_MOMENTUM", 0.99))
+    muon_backend_steps = int(os.environ.get("MUON_BACKEND_STEPS", 5))
+    muon_momentum_warmup_start = float(os.environ.get("MUON_MOMENTUM_WARMUP_START", 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get("MUON_MOMENTUM_WARMUP_STEPS", 1500))
+    momentum_cyclic = bool(int(os.environ.get("MOMENTUM_CYCLIC", "1")))
+    momentum_min = float(os.environ.get("MOMENTUM_MIN", 0.85))
+    momentum_max = float(os.environ.get("MOMENTUM_MAX", 0.95))
+    momentum_cycle_period = int(os.environ.get("MOMENTUM_CYCLE_PERIOD", 50))
+    beta1 = float(os.environ.get("BETA1", 0.9))
+    beta2 = float(os.environ.get("BETA2", 0.95))
+    adam_eps = float(os.environ.get("ADAM_EPS", 1e-8))
+    grad_clip_norm = float(os.environ.get("GRAD_CLIP_NORM", 0.3))
+    weight_decay = float(os.environ.get("WEIGHT_DECAY", 0.04))
+
+    eval_stride = int(os.environ.get("EVAL_STRIDE", 64))
+    eval_batch_seqs = int(os.environ.get("EVAL_BATCH_SEQS", 32))
+
+    bigram_vocab_size = int(os.environ.get("BIGRAM_VOCAB_SIZE", 10240))
+    bigram_dim = int(os.environ.get("BIGRAM_DIM", 128))
+
+    swa_enabled = bool(int(os.environ.get("SWA_ENABLED", "1")))
+    swa_start_frac = float(os.environ.get("SWA_START_FRAC", 0.4))
+    swa_every = int(os.environ.get("SWA_EVERY", 50))
+
+# -----------------------------
+# MUON OPTIMIZER
+# -----------------------------
+
+def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int, nesterov: bool = True, weight_decay: float = 0.0):
+        super().__init__(
+            params,
+            dict(lr=lr, momentum=momentum, backend_steps=backend_steps, nesterov=nesterov, weight_decay=weight_decay),
+        )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr = group["lr"]
+            momentum = group["momentum"]
+            backend_steps = group["backend_steps"]
+            nesterov = group["nesterov"]
+
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr : curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+
+            wd = group.get("weight_decay", 0.0)
+            curr = 0
+            for p in params:
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                if wd > 0:
+                    p.data.mul_(1.0 - lr * wd)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+
+# -----------------------------
+# TOKENIZER-AGNOSTIC EVALUATION
+# -----------------------------
+
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}")
+    return tokens[: usable + 1]
+
+
+def eval_val(
+    args: Hyperparameters,
+    model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    grad_accum_steps: int,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+) -> tuple[float, float]:
+    local_batch_tokens = args.val_batch_size // (world_size * grad_accum_steps)
+    if local_batch_tokens < args.train_seq_len:
+        raise ValueError(
+            "VAL_BATCH_SIZE must provide at least one sequence per rank; "
+            f"got VAL_BATCH_SIZE={args.val_batch_size}, WORLD_SIZE={world_size}, "
+            f"GRAD_ACCUM_STEPS={grad_accum_steps}, TRAIN_SEQ_LEN={args.train_seq_len}"
+        )
+    local_batch_seqs = local_batch_tokens // args.train_seq_len
+    total_seqs = (val_tokens.numel() - 1) // args.train_seq_len
+    seq_start = (total_seqs * rank) // world_size
+    seq_end = (total_seqs * (rank + 1)) // world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * args.train_seq_len
+            raw_end = batch_seq_end * args.train_seq_len + 1
+            local = val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, args.train_seq_len)
+            y = local[1:].reshape(-1, args.train_seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (has_leading_space_lut[tgt_ids] & ~is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+    val_loss = val_loss_sum / val_token_count
+    bits_per_token = val_loss.item() / math.log(2.0)
+    tokens_per_byte = val_token_count.item() / val_byte_count.item()
+    model.train()
+    return float(val_loss.item()), float(bits_per_token * tokens_per_byte)
+
+
+# -----------------------------
+# POST-TRAINING QUANTIZATION (INT8 legacy + INT6 mixed)
+# -----------------------------
+
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "CONTROL_TENSOR_NAME_PATTERNS",
+        "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,smear,bigram.scale",
+    ).split(",")
+    if pattern
+)
+FP16_KEEP_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get("FP16_KEEP_NAME_PATTERNS", "tok_emb,blocks.8.attn.c_k").split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_FP32_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "INT8_KEEP_FLOAT_FP32_NAME_PATTERNS",
+        ",".join(CONTROL_TENSOR_NAME_PATTERNS),
+    ).split(",")
+    if pattern
+)
+INT8_KEEP_FLOAT_MAX_NUMEL = 65_536
+INT8_KEEP_FLOAT_STORE_DTYPE = torch.float16
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_PERCENTILE = 99.99984
+INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0
+
+def tensor_nbytes(t: Tensor) -> int:
+    return int(t.numel()) * int(t.element_size())
+
+def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        clip_abs = (
+            torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1)
+            if t32.numel()
+            else torch.empty((t32.shape[0],), dtype=torch.float32)
+        )
+        clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None])
+        scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0)
+        q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous()
+        return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+    clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous()
+    return q, scale
+
+
+def _classify_param(name: str) -> str:
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if "bigram" in name:
+        return "bigram"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+
+def quantize_intN_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        row_max = t32.abs().amax(dim=1)
+        scale = (row_max / clip_range).clamp_min(1e-12).to(torch.float16)
+        scale = scale.clamp_min(torch.finfo(torch.float16).tiny)
+        q = torch.clamp(torch.round(t32 / scale.float()[:, None]), -(clip_range+1), clip_range).to(torch.int8)
+        return q, scale
+    amax = t32.abs().max().item()
+    scale = torch.tensor(max(amax / clip_range, 1e-12), dtype=torch.float16)
+    q = torch.clamp(torch.round(t32 / scale.float()), -(clip_range+1), clip_range).to(torch.int8)
+    return q, scale
+
+def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]):
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = _classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 8192:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if any(pattern in name for pattern in FP16_KEEP_NAME_PATTERNS):
+            result[name] = t.to(dtype=torch.float16).contiguous()
+            meta[name] = "passthrough_fp16"
+            continue
+        if cat in int6_cats and t.ndim >= 1:
+            clip = 15 if cat == "mlp" else 31  # int5 for MLP, int6 for attention
+            q, s = quantize_intN_per_row(t, clip_range=clip)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": f"int{5 if cat == 'mlp' else 6}"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    return result, meta
+
+def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object],
+                          template_sd: dict[str, Tensor]) -> dict[str, Tensor]:
+    out: dict[str, Tensor] = {}
+    for name, orig in template_sd.items():
+        info = meta[name]
+        orig_dtype = orig.dtype
+        if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"):
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + ".q"], result[name + ".scale"]
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+
+
+# -----------------------------
+# DATA LOADING
+# -----------------------------
+
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    expected_size = header_bytes + num_tokens * token_bytes
+    if file.stat().st_size != expected_size:
+        raise ValueError(f"Shard size mismatch for {file}: expected {expected_size} bytes")
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    if tokens_np.size != num_tokens:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+class TokenStream:
+    def __init__(self, pattern: str):
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self.file_idx = 0
+        self.tokens = load_data_shard(self.files[0])
+        self.pos = 0
+
+    def _advance_file(self) -> None:
+        self.file_idx = (self.file_idx + 1) % len(self.files)
+        self.tokens = load_data_shard(self.files[self.file_idx])
+        self.pos = 0
+
+    def take(self, n: int) -> Tensor:
+        chunks: list[Tensor] = []
+        remaining = n
+        while remaining > 0:
+            avail = self.tokens.numel() - self.pos
+            if avail <= 0:
+                self._advance_file()
+                continue
+            k = min(remaining, avail)
+            chunks.append(self.tokens[self.pos : self.pos + k])
+            self.pos += k
+            remaining -= k
+        return chunks[0] if len(chunks) == 1 else torch.cat(chunks)
+
+
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.stream = TokenStream(pattern)
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        per_rank_span = local_tokens + 1
+        chunk = self.stream.take(per_rank_span * self.world_size)
+        start = self.rank * per_rank_span
+        local = chunk[start : start + per_rank_span].to(dtype=torch.int64)
+        x = local[:-1].reshape(-1, seq_len)
+        y = local[1:].reshape(-1, seq_len)
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+
+# -----------------------------
+# TRANSFORMER MODULES
+# -----------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+
+class CastedLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+
+
+def restore_low_dim_params_to_fp32(module: nn.Module) -> None:
+    with torch.no_grad():
+        for name, param in module.named_parameters():
+            if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+                param.data = param.data.float()
+
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
+            freqs = torch.outer(t, self.inv_freq.to(device))
+            self._cos_cached = freqs.cos()[None, None, :, :]
+            self._sin_cached = freqs.sin()[None, None, :, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        if dim % num_heads != 0:
+            raise ValueError("model_dim must be divisible by num_heads")
+        if num_heads % num_kv_heads != 0:
+            raise ValueError("num_heads must be divisible by num_kv_heads")
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        if self.head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rotary = Rotary(self.head_dim, base=rope_base)
+
+    def forward(self, x: Tensor) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.c_v(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin)
+        k = apply_rotary_emb(k, cos, sin)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, :, None, None]
+        y = F.scaled_dot_product_attention(
+            q, k, v, attn_mask=None, is_causal=True,
+            enable_gqa=(self.num_kv_heads != self.num_heads),
+        )
+        y = y.transpose(1, 2).contiguous().reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, mlp_mult: float):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = torch.relu(self.fc(x))
+        return self.proj(x.square())
+
+
+class SmearGate(nn.Module):
+    """Blend each token's embedding with the previous token's embedding."""
+    def __init__(self, dim: int):
+        super().__init__()
+        self.gate = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
+
+    def forward(self, x: Tensor) -> Tensor:
+        g = torch.sigmoid(self.gate.to(dtype=x.dtype))[None, None, :]
+        x_prev = torch.cat([torch.zeros_like(x[:, :1]), x[:, :-1]], dim=1)
+        return (1 - g) * x + g * x_prev
+
+
+class BigramHashEmbedding(nn.Module):
+    """Hash consecutive token pairs into a learned embedding table."""
+    def __init__(self, bigram_vocab_size: int, bigram_dim: int, model_dim: int):
+        super().__init__()
+        self.bigram_vocab_size = bigram_vocab_size
+        self.embed = nn.Embedding(bigram_vocab_size, bigram_dim)
+        nn.init.zeros_(self.embed.weight)
+        self.proj = CastedLinear(bigram_dim, model_dim, bias=False) if bigram_dim != model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale = nn.Parameter(torch.tensor(0.05, dtype=torch.float32))
+
+    def bigram_hash(self, tokens: Tensor) -> Tensor:
+        t = tokens.to(torch.int32)
+        mod = self.bigram_vocab_size - 1
+        out = torch.empty_like(t)
+        out[..., 0] = mod
+        out[..., 1:] = torch.bitwise_xor(36313 * t[..., 1:], 27191 * t[..., :-1]) % mod
+        return out.long()
+
+    def forward(self, token_ids: Tensor) -> Tensor:
+        h = self.embed(self.bigram_hash(token_ids))
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: float, rope_base: float, qk_gain_init: float):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+
+    def forward(self, x: Tensor, x0: Tensor) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x))
+        x = x + self.attn_scale.to(dtype=x.dtype)[None, None, :] * attn_out
+        x = x + self.mlp_scale.to(dtype=x.dtype)[None, None, :] * self.mlp(self.mlp_norm(x))
+        return x
+
+
+class GPT(nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        num_layers: int,
+        model_dim: int,
+        num_heads: int,
+        num_kv_heads: int,
+        mlp_mult: float,
+        tie_embeddings: bool,
+        tied_embed_init_std: float,
+        logit_softcap: float,
+        rope_base: float,
+        qk_gain_init: float,
+        bigram_vocab_size: int = 0,
+        bigram_dim: int = 128,
+        unique_layers: int = 0,
+    ):
+        super().__init__()
+        if logit_softcap <= 0.0:
+            raise ValueError(f"logit_softcap must be positive, got {logit_softcap}")
+        self.tie_embeddings = tie_embeddings
+        self.tied_embed_init_std = tied_embed_init_std
+        self.logit_softcap = logit_softcap
+        self.tok_emb = nn.Embedding(vocab_size, model_dim)
+        self.bigram = BigramHashEmbedding(bigram_vocab_size, bigram_dim, model_dim) if bigram_vocab_size > 0 else None
+        self.num_encoder_layers = num_layers // 2
+        self.num_decoder_layers = num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, model_dim, dtype=torch.float32))
+        self.smear = SmearGate(model_dim)
+        # Layer sharing: create unique_layers physical blocks, map num_layers virtual layers to them
+        n_unique = unique_layers if unique_layers > 0 else num_layers
+        self.blocks = nn.ModuleList(
+            [
+                Block(model_dim, num_heads, num_kv_heads, mlp_mult, rope_base, qk_gain_init)
+                for _ in range(n_unique)
+            ]
+        )
+        # Mapping from virtual layer index → physical block index
+        # Last virtual layer shares with the last physical block
+        self._layer_map = list(range(min(n_unique, num_layers)))
+        while len(self._layer_map) < num_layers:
+            self._layer_map.append(n_unique - 1)
+        self.final_norm = RMSNorm()
+        self.lm_head = None if tie_embeddings else CastedLinear(model_dim, vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        num_layers = len(self._layer_map)  # virtual depth, not physical blocks
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, "_zero_init", False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+                    if ".proj." in name or name.endswith(".proj"):
+                        with torch.no_grad():
+                            module.weight.mul_(1.0 / math.sqrt(2 * num_layers))
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x).reshape(-1, x.size(-1))
+        targets = target_ids.reshape(-1)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            if self.lm_head is None:
+                raise RuntimeError("lm_head is required when tie_embeddings=False")
+            logits_proj = self.lm_head(x)
+        logits = self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+        return F.cross_entropy(logits.float(), targets, reduction="mean")
+
+    def forward_logits(self, input_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        if self.bigram is not None:
+            x = x + self.bigram(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        x = self.smear(x)
+        x0 = x
+        skips: list[Tensor] = []
+        for i in range(self.num_encoder_layers):
+            x = self.blocks[self._layer_map[i]](x, x0)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            if skips:
+                x = x + self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+            x = self.blocks[self._layer_map[self.num_encoder_layers + i]](x, x0)
+        x = self.final_norm(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+
+
+def eval_val_sliding(
+    args: Hyperparameters,
+    base_model: nn.Module,
+    rank: int,
+    world_size: int,
+    device: torch.device,
+    val_tokens: Tensor,
+    base_bytes_lut: Tensor,
+    has_leading_space_lut: Tensor,
+    is_boundary_token_lut: Tensor,
+    stride: int,
+    batch_seqs: int = 32,
+) -> tuple[float, float]:
+    seq_len = args.train_seq_len
+    total_tokens = val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, stride)
+                     if min(ws + seq_len, total_tokens) - ws >= stride or ws == 0]
+    total_windows = len(window_starts)
+    my_s = (total_windows * rank) // world_size
+    my_e = (total_windows * (rank + 1)) // world_size
+    my_windows = window_starts[my_s:my_e]
+
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    base_model.eval()
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens: list[int] = []
+            for i, ws in enumerate(batch_ws):
+                end = min(ws + seq_len, total_tokens)
+                wlen = end - ws
+                wlens.append(wlen)
+                chunk = val_tokens[ws:end + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits = base_model.forward_logits(x_batch)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else max(wlen - stride, 0)
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = base_bytes_lut[tgt].to(torch.float64)
+                tb += (has_leading_space_lut[tgt] & ~is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+            if rank == 0 and (bi // batch_seqs) % 50 == 0:
+                done = min(bi + batch_seqs, len(my_windows))
+                pct = done / len(my_windows) * 100
+                running_bpb = 0.0
+                if token_count.item() > 0:
+                    rl = (loss_sum / token_count).item()
+                    running_bpb = rl / math.log(2.0) * (token_count.item() / byte_count.item())
+                print(f"  sliding_eval [{pct:5.1f}%] {done}/{len(my_windows)} windows running_bpb={running_bpb:.6f}", flush=True)
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    val_loss = (loss_sum / token_count).item()
+    bits_per_token = val_loss / math.log(2.0)
+    tokens_per_byte = token_count.item() / byte_count.item()
+    base_model.train()
+    return val_loss, bits_per_token * tokens_per_byte
+
+
+# -----------------------------
+# CO-OCCURRENCE QK INIT
+# -----------------------------
+
+def build_cooccurrence_matrix(data_pattern: str, vocab_size: int, max_tokens: int = 2_000_000) -> Tensor:
+    """Build a vocab×vocab bigram co-occurrence matrix from training data. Fast: ~1s for 2M tokens."""
+    stream = TokenStream(data_pattern)
+    tokens = stream.take(min(max_tokens, stream.tokens.numel()))
+    tokens_np = tokens.numpy().astype(np.int64)
+    C = np.zeros((vocab_size, vocab_size), dtype=np.float64)
+    for i in range(len(tokens_np) - 1):
+        C[tokens_np[i], tokens_np[i + 1]] += 1.0
+    # Symmetrize: both (i,j) and (j,i) indicate co-occurrence
+    C = C + C.T
+    # Log-PMI-like transform: log(C + 1) then center rows and columns
+    C = np.log1p(C)
+    C -= C.mean(axis=1, keepdims=True)
+    C -= C.mean(axis=0, keepdims=True)
+    return torch.from_numpy(C).float()
+
+
+def init_qk_from_cooccurrence(model: nn.Module, cooc: Tensor, num_heads: int, num_kv_heads: int, head_dim: int) -> None:
+    """Initialize W_Q and W_K in layer 0 so that Q·K^T approximates the co-occurrence structure.
+    SVD the co-occurrence matrix, distribute top components across heads."""
+    U, S, Vt = torch.linalg.svd(cooc, full_matrices=False)
+    # We need num_heads * head_dim components for Q, num_kv_heads * head_dim for K
+    q_dim = num_heads * head_dim
+    k_dim = num_kv_heads * head_dim
+    n_components = max(q_dim, k_dim)
+    # Scale by sqrt(S) so that Q·K^T ≈ U·S·V^T
+    S_sqrt = S[:n_components].sqrt()
+    Q_init = U[:, :q_dim] * S_sqrt[:q_dim].unsqueeze(0)  # [vocab, q_dim]
+    K_init = Vt[:k_dim, :].T * S_sqrt[:k_dim].unsqueeze(0)  # [vocab, k_dim]
+    # But W_Q maps from model_dim (=embedding dim at layer 0) to q_dim
+    # At init, hidden states ≈ embeddings. We need W_Q such that E @ W_Q ≈ Q_init
+    # Since E is ~orthogonal at init, W_Q ≈ E^T @ Q_init (pseudo-inverse)
+    # However, E is vocab×dim, not square. Use E's pseudoinverse.
+    # Simpler approach: directly set W_Q weight rows from SVD components
+    # W_Q: [q_dim, model_dim]. At step 0: q = x @ W_Q^T = E[tok] @ W_Q^T
+    # We want the dot product q_i · k_j to reflect co-occurrence.
+    # Set W_Q = Q_init^T (projected to model_dim) — but Q_init is [vocab, q_dim]
+    # This doesn't directly work because W_Q operates on dim, not vocab.
+    #
+    # Correct approach: W_Q and W_K are [out_dim, model_dim].
+    # q = h @ W_Q^T where h is the hidden state (≈ embedding at layer 0)
+    # We want: (h_i @ W_Q^T) @ (W_K @ h_j^T) = h_i^T @ W_Q^T @ W_K @ h_j ≈ C[tok_i, tok_j]
+    # So W_Q^T @ W_K ≈ E_pinv @ C @ E_pinv^T where E_pinv is pseudoinverse of E
+    # But E is not available at model construction time (it's a parameter).
+    # Instead, use a simpler signal: set W_Q and W_K from SVD of C projected through
+    # a random projection (since E is random at init).
+    # Actually simplest: just scale the existing orthogonal init by the SVD singular values
+    # to bias different heads toward different co-occurrence patterns.
+
+    # Practical approach: for layer 0, replace W_Q and W_K with SVD-derived weights
+    # We project the SVD components into model_dim space via random projection
+    block0 = list(model.blocks.children())[0] if hasattr(model, 'blocks') else None
+    if block0 is None:
+        return
+    model_dim = block0.attn.c_q.weight.shape[1]
+    # Random projection from vocab_size to model_dim (fixed seed for reproducibility)
+    rng = torch.Generator()
+    rng.manual_seed(12345)
+    P = torch.randn(cooc.shape[0], model_dim, generator=rng) / math.sqrt(model_dim)
+    # Project co-occurrence into model_dim space: C_proj = P^T @ C @ P [model_dim, model_dim]
+    C_proj = P.T @ cooc @ P
+    # SVD of projected matrix
+    U2, S2, Vt2 = torch.linalg.svd(C_proj, full_matrices=False)
+    S2_sqrt = S2[:n_components].clamp_min(0).sqrt()
+    # W_Q rows = U2 columns scaled by sqrt(S), reshaped to [q_dim, model_dim]
+    # W_K rows = V2 columns scaled by sqrt(S), reshaped to [k_dim, model_dim]
+    with torch.no_grad():
+        q_weight = (U2[:, :q_dim] * S2_sqrt[:q_dim].unsqueeze(0)).T  # [q_dim, model_dim]
+        k_weight = (Vt2[:k_dim, :].T * S2_sqrt[:k_dim].unsqueeze(0)).T  # [k_dim, model_dim]
+        # Normalize to match the scale of orthogonal init
+        q_scale = block0.attn.c_q.weight.data.norm() / q_weight.norm().clamp_min(1e-8)
+        k_scale = block0.attn.c_k.weight.data.norm() / k_weight.norm().clamp_min(1e-8)
+        block0.attn.c_q.weight.data.copy_(q_weight.to(device=block0.attn.c_q.weight.device, dtype=block0.attn.c_q.weight.dtype) * q_scale)
+        block0.attn.c_k.weight.data.copy_(k_weight.to(device=block0.attn.c_k.weight.device, dtype=block0.attn.c_k.weight.dtype) * k_scale)
+
+
+# -----------------------------
+# TRAINING
+# -----------------------------
+
+def main() -> None:
+    global zeropower_via_newtonschulz5
+
+    code = Path(__file__).read_text(encoding="utf-8")
+    args = Hyperparameters()
+    zeropower_via_newtonschulz5 = torch.compile(zeropower_via_newtonschulz5)
+
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral")
+    grad_accum_steps = 8 // world_size
+    grad_scale = 1.0 / grad_accum_steps
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    master_process = rank == 0
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+
+    logfile = None
+    if master_process:
+        os.makedirs("logs", exist_ok=True)
+        logfile = f"logs/{args.run_id}.txt"
+        print(logfile)
+
+    def log0(msg: str, console: bool = True) -> None:
+        if not master_process:
+            return
+        if console:
+            print(msg)
+        if logfile is not None:
+            with open(logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+    log0(code, console=False)
+    log0("=" * 100, console=False)
+    log0(f"Running Python {sys.version}", console=False)
+    log0(f"Running PyTorch {torch.__version__}", console=False)
+    log0(
+        subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout,
+        console=False,
+    )
+    log0("=" * 100, console=False)
+
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.cuda.manual_seed_all(args.seed)
+
+    if not args.tokenizer_path.endswith(".model"):
+        raise ValueError(f"Script only setup for SentencePiece .model file: {args.tokenizer_path}")
+    sp = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
+    if int(sp.vocab_size()) != args.vocab_size:
+        raise ValueError(
+            f"VOCAB_SIZE={args.vocab_size} does not match tokenizer vocab_size={int(sp.vocab_size())}"
+        )
+    dataset_dir = Path(args.data_path).resolve()
+    actual_train_files = len(list(dataset_dir.glob("fineweb_train_*.bin")))
+    val_tokens = load_validation_tokens(args.val_files, args.train_seq_len)
+    base_bytes_lut, has_leading_space_lut, is_boundary_token_lut = build_sentencepiece_luts(
+        sp, args.vocab_size, device
+    )
+    log0(f"val_bpb:enabled tokenizer_kind=sentencepiece tokenizer_path={args.tokenizer_path}")
+    log0(f"train_loader:dataset:{dataset_dir.name} train_shards:{actual_train_files}")
+    log0(f"val_loader:shards pattern={args.val_files} tokens:{val_tokens.numel() - 1}")
+
+    # MODEL + OPTIMIZER SETUP
+    base_model = GPT(
+        vocab_size=args.vocab_size,
+        num_layers=args.num_layers,
+        model_dim=args.model_dim,
+        num_heads=args.num_heads,
+        num_kv_heads=args.num_kv_heads,
+        mlp_mult=args.mlp_mult,
+        tie_embeddings=args.tie_embeddings,
+        tied_embed_init_std=args.tied_embed_init_std,
+        logit_softcap=args.logit_softcap,
+        rope_base=args.rope_base,
+        qk_gain_init=args.qk_gain_init,
+        bigram_vocab_size=args.bigram_vocab_size,
+        bigram_dim=args.bigram_dim,
+        unique_layers=args.unique_layers,
+    ).to(device).bfloat16()
+    for module in base_model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    restore_low_dim_params_to_fp32(base_model)
+
+    # Co-occurrence QK init for layer 0
+    cooc_init_enabled = bool(int(os.environ.get("COOC_QK_INIT", "1")))
+    if cooc_init_enabled and master_process:
+        log0("cooc_qk_init: building co-occurrence matrix from training data...")
+    if cooc_init_enabled:
+        cooc_matrix = build_cooccurrence_matrix(args.train_files, args.vocab_size, max_tokens=2_000_000)
+        init_qk_from_cooccurrence(
+            base_model, cooc_matrix,
+            num_heads=args.num_heads, num_kv_heads=args.num_kv_heads,
+            head_dim=args.model_dim // args.num_heads,
+        )
+        if master_process:
+            log0("cooc_qk_init: done, W_Q and W_K in layer 0 initialized from co-occurrence SVD")
+
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    model: nn.Module = DDP(compiled_model, device_ids=[local_rank], broadcast_buffers=False) if distributed else compiled_model
+
+    block_named_params = list(base_model.blocks.named_parameters())
+    matrix_params = [
+        p for name, p in block_named_params
+        if p.ndim == 2 and not any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    scalar_params = [
+        p for name, p in block_named_params
+        if p.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)
+    ]
+    if base_model.skip_weights.numel() > 0:
+        scalar_params.append(base_model.skip_weights)
+    scalar_params.append(base_model.smear.gate)
+    if base_model.bigram is not None:
+        scalar_params.append(base_model.bigram.scale)
+
+    token_lr = args.tied_embed_lr if args.tie_embeddings else args.embed_lr
+    tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}]
+    if base_model.bigram is not None:
+        tok_params.append({"params": [base_model.bigram.embed.weight], "lr": token_lr, "base_lr": token_lr})
+        if base_model.bigram.proj is not None:
+            matrix_params.append(base_model.bigram.proj.weight)
+
+    optimizer_tok = torch.optim.AdamW(
+        tok_params,
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizer_muon = Muon(
+        matrix_params,
+        lr=args.matrix_lr,
+        momentum=args.muon_momentum,
+        backend_steps=args.muon_backend_steps,
+        weight_decay=0.04,
+    )
+    for group in optimizer_muon.param_groups:
+        group["base_lr"] = args.matrix_lr
+    optimizer_scalar = torch.optim.AdamW(
+        [{"params": scalar_params, "lr": args.scalar_lr, "base_lr": args.scalar_lr}],
+        betas=(args.beta1, args.beta2),
+        eps=args.adam_eps,
+        weight_decay=args.weight_decay,
+        fused=True,
+    )
+    optimizers: list[torch.optim.Optimizer] = [optimizer_tok, optimizer_muon, optimizer_scalar]
+    if base_model.lm_head is not None:
+        optimizer_head = torch.optim.Adam(
+            [{"params": [base_model.lm_head.weight], "lr": args.head_lr, "base_lr": args.head_lr}],
+            betas=(args.beta1, args.beta2),
+            eps=args.adam_eps,
+            fused=True,
+        )
+        optimizers.insert(1, optimizer_head)
+
+    n_params = sum(p.numel() for p in base_model.parameters())
+    log0(f"model_params:{n_params}")
+    log0(f"world_size:{world_size} grad_accum_steps:{grad_accum_steps}")
+    log0(f"attention_mode:gqa num_heads:{args.num_heads} num_kv_heads:{args.num_kv_heads}")
+    log0(
+        f"tie_embeddings:{args.tie_embeddings} embed_lr:{token_lr} "
+        f"matrix_lr:{args.matrix_lr} scalar_lr:{args.scalar_lr}"
+    )
+    log0(
+        f"train_batch_tokens:{args.train_batch_tokens} train_seq_len:{args.train_seq_len} "
+        f"iterations:{args.iterations} warmup_steps:{args.warmup_steps} "
+        f"max_wallclock_seconds:{args.max_wallclock_seconds:.3f}"
+    )
+    log0(f"seed:{args.seed}")
+
+    # DATA LOADER & MODEL WARMUP
+    train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    def zero_grad_all() -> None:
+        for opt in optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    max_wallclock_ms = 1000.0 * args.max_wallclock_seconds if args.max_wallclock_seconds > 0 else None
+
+    def lr_mul(step: int, elapsed_ms: float) -> float:
+        if args.warmdown_iters <= 0:
+            return 1.0
+        if max_wallclock_ms is None:
+            warmdown_start = max(args.iterations - args.warmdown_iters, 0)
+            return max((args.iterations - step) / max(args.warmdown_iters, 1), 0.0) if warmdown_start <= step < args.iterations else 1.0
+        step_ms = elapsed_ms / max(step, 1)
+        warmdown_ms = args.warmdown_iters * step_ms
+        remaining_ms = max(max_wallclock_ms - elapsed_ms, 0.0)
+        return remaining_ms / max(warmdown_ms, 1e-9) if remaining_ms <= warmdown_ms else 1.0
+
+    if args.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(args.warmup_steps):
+            zero_grad_all()
+            for micro_step in range(grad_accum_steps):
+                if distributed:
+                    model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+                x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+                with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                    warmup_loss = model(x, y)
+                (warmup_loss * grad_scale).backward()
+            for opt in optimizers:
+                opt.step()
+            zero_grad_all()
+            if args.warmup_steps <= 20 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == args.warmup_steps:
+                log0(f"warmup_step:{warmup_step + 1}/{args.warmup_steps}")
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        zero_grad_all()
+        if distributed:
+            model.require_backward_grad_sync = True
+        train_loader = DistributedTokenLoader(args.train_files, rank, world_size, device)
+
+    # MAIN TRAINING LOOP
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    swa_state: dict[str, Tensor] | None = None
+    swa_count = 0
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    step = 0
+    while True:
+        last_step = step == args.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        should_validate = last_step or (args.val_loss_every > 0 and step % args.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(
+                args, model, rank, world_size, device, grad_accum_steps,
+                val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            )
+            log0(
+                f"step:{step}/{args.iterations} val_loss:{val_loss:.4f} val_bpb:{val_bpb:.4f} "
+                f"train_time:{training_time_ms:.0f}ms step_avg:{training_time_ms / max(step, 1):.2f}ms"
+            )
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < args.iterations:
+                log0(
+                    f"stopping_early: wallclock_cap train_time:{training_time_ms:.0f}ms "
+                    f"step:{step}/{args.iterations}"
+                )
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        scale = lr_mul(step, elapsed_ms)
+        zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+        for micro_step in range(grad_accum_steps):
+            if distributed:
+                model.require_backward_grad_sync = micro_step == grad_accum_steps - 1
+            x, y = train_loader.next_batch(args.train_batch_tokens, args.train_seq_len, grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss * grad_scale).backward()
+        train_loss /= grad_accum_steps
+
+        if step < args.muon_momentum_warmup_steps:
+            # Linear warmup phase
+            frac = step / args.muon_momentum_warmup_steps if args.muon_momentum_warmup_steps > 0 else 1.0
+            muon_momentum = (1 - frac) * args.muon_momentum_warmup_start + frac * args.muon_momentum
+        elif args.momentum_cyclic:
+            # Cyclic momentum: triangle wave between momentum_min and momentum_max
+            period = args.momentum_cycle_period * 2
+            pos = (step % period) / period
+            if pos < 0.5:
+                muon_momentum = args.momentum_min + (args.momentum_max - args.momentum_min) * (pos * 2)
+            else:
+                muon_momentum = args.momentum_max - (args.momentum_max - args.momentum_min) * ((pos - 0.5) * 2)
+        else:
+            muon_momentum = args.muon_momentum
+        for group in optimizer_muon.param_groups:
+            group["momentum"] = muon_momentum
+
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group["lr"] = group["base_lr"] * scale
+
+        if args.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), args.grad_clip_norm)
+        for opt in optimizers:
+            opt.step()
+        zero_grad_all()
+
+        step += 1
+        approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+
+        # SWA: collect checkpoints during warmdown
+        if args.swa_enabled and scale < args.swa_start_frac and step % args.swa_every == 0:
+            if swa_state is None:
+                swa_state = {name: t.detach().cpu().clone() for name, t in base_model.state_dict().items()}
+                swa_count = 1
+                log0(f"swa:start step:{step}")
+            else:
+                for name, t in base_model.state_dict().items():
+                    swa_state[name] += t.detach().cpu()
+                swa_count += 1
+
+        should_log_train = (
+            args.train_log_every > 0
+            and (step <= 10 or step % args.train_log_every == 0 or stop_after_step is not None)
+        )
+        if should_log_train:
+            log0(
+                f"step:{step}/{args.iterations} train_loss:{train_loss.item():.4f} "
+                f"train_time:{approx_training_time_ms:.0f}ms step_avg:{approx_training_time_ms / step:.2f}ms"
+            )
+
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log0(
+        f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+
+    # Apply SWA if collected
+    if args.swa_enabled and swa_state is not None and swa_count > 1:
+        log0(f"swa:applying averaged {swa_count} checkpoints")
+        current_state = base_model.state_dict()
+        avg_state = {
+            name: (tensor / swa_count).to(dtype=current_state[name].dtype)
+            for name, tensor in swa_state.items()
+        }
+        base_model.load_state_dict(avg_state, strict=True)
+
+    # SERIALIZATION + ROUNDTRIP VALIDATION
+    if master_process:
+        torch.save(base_model.state_dict(), "final_model.pt")
+        model_bytes = os.path.getsize("final_model.pt")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model: {model_bytes} bytes")
+        log0(f"Code size: {code_bytes} bytes")
+        log0(f"Total submission size: {model_bytes + code_bytes} bytes")
+
+    # Magnitude pruning: zero out smallest weights to improve compression
+    with torch.no_grad():
+        for name, param in base_model.named_parameters():
+            if param.ndim == 2 and param.numel() > 65536:
+                threshold = torch.quantile(param.abs().float().flatten(), 0.03)
+                mask = param.abs() < threshold
+                param.masked_fill_(mask, 0.0)
+
+    # AWQ: Activation-aware weight scaling before quantization
+    awq_alpha = float(os.environ.get("AWQ_ALPHA", 0.5))
+    awq_enabled = bool(int(os.environ.get("AWQ_ENABLED", "1")))
+    if awq_enabled:
+        log0(f"awq:calibrating alpha={awq_alpha}")
+        # Step 1: Collect per-channel activation magnitudes via hooks
+        act_stats: dict[str, Tensor] = {}
+        hooks = []
+        def make_hook(name):
+            def hook_fn(module, input, output):
+                x = input[0] if isinstance(input, tuple) else input
+                if x.ndim >= 2:
+                    # Mean absolute activation per channel (last dim)
+                    s = x.detach().float().abs().mean(dim=tuple(range(x.ndim - 1)))
+                    if name in act_stats:
+                        act_stats[name] = act_stats[name] + s
+                    else:
+                        act_stats[name] = s
+            return hook_fn
+
+        for name, module in base_model.named_modules():
+            if isinstance(module, (nn.Linear, CastedLinear)):
+                hooks.append(module.register_forward_hook(make_hook(name)))
+
+        # Step 2: Run calibration batches (use val data, 4 batches)
+        base_model.eval()
+        n_calib_batches = 4
+        calib_seq_len = args.train_seq_len
+        calib_batch_seqs = 16
+        calib_tokens_per_batch = calib_batch_seqs * calib_seq_len
+        with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            for cb in range(n_calib_batches):
+                start = cb * calib_tokens_per_batch
+                end = start + calib_tokens_per_batch + 1
+                if end > val_tokens.numel():
+                    break
+                local = val_tokens[start:end].to(device=device, dtype=torch.int64)
+                x = local[:-1].reshape(-1, calib_seq_len)
+                base_model.forward_logits(x)
+
+        for h in hooks:
+            h.remove()
+
+        # Normalize activation stats
+        for name in act_stats:
+            act_stats[name] = act_stats[name] / n_calib_batches
+
+        # Step 3: Scale weight columns by s^alpha
+        awq_scales = {}
+        with torch.no_grad():
+            for name, module in base_model.named_modules():
+                if isinstance(module, (nn.Linear, CastedLinear)) and name in act_stats:
+                    s = act_stats[name].to(module.weight.device)
+                    s = s.clamp_min(1e-6)
+                    scale = s.pow(awq_alpha)
+                    # Scale weight columns (input channels)
+                    if module.weight.shape[1] == scale.shape[0]:
+                        module.weight.data = module.weight.data * scale.unsqueeze(0)
+                        awq_scales[name] = scale.cpu()
+                        # Store inverse scale to apply to inputs at inference
+                        # We'll fold this into the state dict
+
+        log0(f"awq:scaled {len(awq_scales)} layers")
+
+    # INT6 mixed quantization + zstd/zlib export
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()}
+    # Store AWQ inverse scales in the state dict for inference compensation
+    if awq_enabled and awq_scales:
+        for lname, scale in awq_scales.items():
+            sd_cpu[f"_awq_inv_scale.{lname}"] = (1.0 / scale).to(torch.float16)
+    quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn", "bigram"})
+    quant_buf = io.BytesIO()
+    torch.save({"w": quant_result, "m": quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    if _COMPRESSOR == "zstd":
+        quant_blob = zstandard.ZstdCompressor(level=22).compress(quant_raw)
+    else:
+        quant_blob = zlib.compress(quant_raw, 9)
+    if master_process:
+        with open("final_model.int8.ptz", "wb") as f:
+            f.write(quant_blob)
+        quant_file_bytes = os.path.getsize("final_model.int8.ptz")
+        code_bytes = len(code.encode("utf-8"))
+        log0(f"Serialized model int6+{_COMPRESSOR}: {quant_file_bytes} bytes")
+        log0(f"Total submission size int8+zlib: {quant_file_bytes + code_bytes} bytes")
+
+    if distributed:
+        dist.barrier()
+    with open("final_model.int8.ptz", "rb") as f:
+        quant_blob_disk = f.read()
+    if _COMPRESSOR == "zstd":
+        decompressed = zstandard.ZstdDecompressor().decompress(quant_blob_disk)
+    else:
+        decompressed = zlib.decompress(quant_blob_disk)
+    quant_state = torch.load(io.BytesIO(decompressed), map_location="cpu")
+    deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu)
+    # AWQ: undo column scaling after dequantization
+    if awq_enabled:
+        awq_inv_keys = [k for k in deq_state if k.startswith("_awq_inv_scale.")]
+        for inv_key in awq_inv_keys:
+            inv_scale = deq_state.pop(inv_key).float()
+            layer_name = inv_key.replace("_awq_inv_scale.", "")
+            weight_key = layer_name + ".weight"
+            if weight_key in deq_state:
+                # Undo: W_orig = W_scaled * inv_scale (per column)
+                deq_state[weight_key] = deq_state[weight_key].float() * inv_scale.unsqueeze(0)
+                deq_state[weight_key] = deq_state[weight_key].to(dtype=sd_cpu[weight_key].dtype)
+        log0(f"awq:unscaled {len(awq_inv_keys)} layers after dequant")
+    # Remove any remaining AWQ keys before loading
+    deq_state = {k: v for k, v in deq_state.items() if not k.startswith("_awq_")}
+    base_model.load_state_dict(deq_state, strict=True)
+
+    # Sliding window eval on int6-roundtripped weights
+    torch.cuda.synchronize()
+    t_qeval = time.perf_counter()
+    if args.eval_stride > 0 and args.eval_stride < args.train_seq_len:
+        log0(f"final_eval_mode:sliding_window stride:{args.eval_stride} batch_seqs:{args.eval_batch_seqs}")
+        q_val_loss, q_val_bpb = eval_val_sliding(
+            args, base_model, rank, world_size, device,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+            stride=args.eval_stride, batch_seqs=args.eval_batch_seqs,
+        )
+    else:
+        log0("final_eval_mode:standard")
+        q_val_loss, q_val_bpb = eval_val(
+            args, model, rank, world_size, device, grad_accum_steps,
+            val_tokens, base_bytes_lut, has_leading_space_lut, is_boundary_token_lut,
+        )
+    torch.cuda.synchronize()
+    log0(
+        f"final_int8_zlib_roundtrip val_loss:{q_val_loss:.4f} val_bpb:{q_val_bpb:.4f} "
+        f"eval_time:{1000.0 * (time.perf_counter() - t_qeval):.0f}ms"
+    )
+    log0(f"final_int8_zlib_roundtrip_exact val_loss:{q_val_loss:.8f} val_bpb:{q_val_bpb:.8f}")
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
+# fixes applied
+# tuned