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Project Chronos (Experimental)

A storage-aware MoE stack built for SSD+DRAM hybrid inference, with a full six-stage training pipeline.

PyPI License Python

中文文档

Why reactive MoE offload breaks down

Mainstream MoE models such as Mixtral, DeepSeek-MoE, and Qwen-MoE make routing decisions at decode time, token by token. The model discovers which experts it needs only when the next token is already on the critical path. If those experts are not resident in VRAM, generation stalls while weights are moved from SSD to RAM to VRAM.

That is not a tuning issue. It is an architectural mismatch.

Most offload runtimes assume the model was originally designed for full VRAM residency, then try to patch storage pressure afterward. On consumer hardware, that usually means the decode loop ends up paying the IO bill over and over again.

Chronos in one line

Move IO into prefill. Move synchronization down to the expert-event level.

sequenceDiagram
    autonumber
    participant Prompt as Prompt tokens
    participant Intent as IntentClassifier
    participant SSD as Clustered SSD cache
    participant RAM as Pinned RAM
    participant H2D as H2D stream
    participant GPU as Decode compute stream
    participant LR as LookaheadRouter

    rect rgb(236, 248, 255)
        Prompt->>Intent: prefill reads the whole prompt
        Intent->>SSD: predict generation-level expert set
        SSD->>RAM: mmap cluster-packed .ctsr files
        RAM->>H2D: enqueue hot experts before first decode token
        H2D-->>GPU: per-expert ready events
    end

    loop every decode token
        GPU->>LR: block-0 hidden state
        LR->>H2D: prefetch experts for t+1..t+K
        GPU->>GPU: forward token t with resident experts
        GPU->>H2D: wait only on the expert events it needs
    end
Loading

Two things matter here:

  1. Prefill-time loading: PrefillScheduler and IntentClassifier bulk-load predicted experts before the first decode token.
  2. Per-expert event sync (M3): promote_to_vram(blocking=False) records a torch.cuda.Event on the _h2d_stream, and the compute stream waits only on the experts it actually needs. No more stream.synchronize() for the whole system.

Under a simulated 30 ms SSD delay, the new path keeps 35 ms+ per token of pipeline slack compared with the older blocking path.

Three-tier storage (M1: cluster-aware safetensors)

flowchart TB
    subgraph GPU["VRAM / Metal unified memory"]
        Dense["Dense layers"]
        Shared["Shared experts<br/>always resident"]
        Hot["Predicted hot experts<br/>promoted before use"]
        Events["Per-expert CUDA events<br/>no global stream sync"]
    end

    subgraph RAM["Pinned RAM staging tier"]
        Buffer["Prefetched expert tensors"]
        LRU["RAM LRU cache"]
    end

    subgraph Disk["NVMe SSD"]
        C1["cluster_000.ctsr"]
        C2["cluster_001.ctsr"]
        Manifest["cluster_manifest.json<br/>expert -> cluster map"]
    end

    Manifest --> C1
    Manifest --> C2
    C1 -->|"sequential mmap read"| Buffer
    C2 -->|"sequential mmap read"| Buffer
    Buffer --> LRU
    LRU -->|"non-blocking H2D"| Hot
    Hot --> Events
    Shared --> Events
    Dense --> Events
Loading

cluster_manifest.json and .ctsr files are produced offline by Louvain clustering. At runtime, Chronos uses safetensors.safe_open(...).mmap to bring an entire expert cluster into RAM in one shot, turning random reads into mostly sequential reads.

Cache misses degrade gracefully instead of stalling

Even the worst case does not hard-stop generation:

# Pure tensor math: no Python branch, no graph break under torch.compile
output = avail[i] * expert_output + (1.0 - avail[i]) * shared_expert_output

The shared expert is always resident, so generation continues while the missing expert finishes loading in the background. For exact lazy/offload comparison modes, Chronos synchronously materializes only the selected missing expert and evicts low-LRU experts to stay inside the resident budget; it does not silently full-load all experts. Quality degrades smoothly only when fallback mode is explicitly enabled.

Dual-layer routing with supervised lookahead (M2)

flowchart LR
    Prompt["Prompt / prefill context"] --> IC["IntentClassifier<br/>generation-level expert prior"]
    IC --> Budget["ExpertPredictor<br/>budgeted hot set"]
    Budget --> Prefill["PrefillScheduler<br/>bulk preload before decode"]

    Token["Decode token t"] --> Block0["Transformer block 0"]
    Block0 --> LR["LookaheadRouter<br/>Q_t^(1..K)"]
    LR --> Future["Future expert predictions<br/>t+1 ... t+K"]
    Future --> Async["AsyncPrefetcher<br/>prefetch queue"]

    Prefill --> Cache["CacheManager / ExpertStore"]
    Async --> Cache
    Cache --> MoE["ChronosMOEFeedForward<br/>resident expert or shared fallback"]
Loading
IntentClassifier (Layer 1) LookaheadRouter (Layer 2)
When it runs Once during prefill Every token during decode
Input Full prompt (up to 512 tokens) Hidden state after Block 0
Output Expert set for the full generation Expert IDs for t+1, t+2
Training target Supervised from real activation logs L_lookahead supervised by real router decisions at t+k
Parameter count ~10-15M ~2M

Before M2, the lookahead head was just a head with no real supervision. M2 adds a proper soft-target objective:

$$L_{\mathrm{lookahead}} = \frac{1}{|K_{\mathrm{valid}}|} \sum_{k \in K_{\mathrm{valid}}} \mathbb{E}_{b,t} \left[ - \sum_{e=1}^{E} \mathrm{sg}(P_{b,t+k,e}) \log Q_{b,t,e}^{(k)} \right]$$

That turns the lookahead router into an actual predictor of future routing, instead of a best-effort heuristic.

Full training stack: Stage 1 -> Stage 6

Each stage has its own entry script, and every one of them inherits the shared Chronos loss mixer: balance loss, temporal locality loss, lookahead loss, and, for alignment stages, a router KL anchor that keeps RL/DPO updates from destroying cache locality.

flowchart LR
    P1["Stage 1<br/>Pretrain<br/>CE + Chronos mix"] --> P2["Stage 2<br/>SFT<br/>assistant-token CE + mix"]
    P2 --> P3["Stage 3<br/>DPO<br/>preference loss + anchor"]
    P2 --> P4["Stage 4<br/>ORPO<br/>NLL + odds-ratio + anchor"]
    P4 --> P5["Stage 5<br/>GRPO<br/>rollout reward + KL + anchor"]
    P5 --> P6["Stage 6<br/>Distill<br/>KD + CE + anchor"]

    subgraph Shared["Shared Chronos training terms"]
        B["raw load-balance aux"]
        T["temporal locality"]
        LA["supervised lookahead"]
        A["router KL anchor<br/>alignment stages"]
    end

    Shared -. applied to .-> P1
    Shared -. applied to .-> P2
    Shared -. applied to .-> P3
    Shared -. applied to .-> P4
    Shared -. applied to .-> P5
    Shared -. applied to .-> P6
Loading
Stage Script Core objective Router KL anchor (default lambda)
1 Pretrain train_chronos.py CE + balance + temporal + lookahead 0.0 (off)
2 SFT train_chronos_sft.py SFT loss + shared Chronos mix 0.01 (weak)
3 DPO train_chronos_dpo.py DPO log-sigma(beta * logits) + mix 0.10 (strong)
4 ORPO train_chronos_orpo.py NLL + lambda * ORPO term 0.10
5 GRPO train_chronos_grpo.py PG * A - beta * KL with ToyReward or pluggable LMRewardModel 0.10
6 Distill train_chronos_distill.py `alpha * T^2 * KL(student

Training dtype and resource policy:

  • --dtype auto is the default. MPS/MLX resolve to BF16-first for training stability, CUDA/XPU resolve to FP16, and CPU resolves to FP32 unless --dtype float16 or --dtype bfloat16 is set explicitly.
  • CPU training configures PyTorch to use physical cores by default. Override with --cpu_threads or --cpu_budget_percent.
  • On macOS, MPS/MLX training forces DataLoader workers to 0 by default to avoid Metal command-buffer crashes from multiprocessing. CPU/CUDA still use worker processes; advanced users can override the guard with CHRONOS_ALLOW_METAL_DATALOADER_WORKERS=1.
  • Native MLX training pushes UI logs, scalar readouts, and chart points every log_interval steps, and Web UI Stop is checked at each batch boundary.
  • The Web UI writes a warning-only <checkpoint>.verify.json after each stage. It checks no-mask vs all-available MoE parity and, on Apple Silicon, MLX prefill logits against the PyTorch CPU baseline.

Native MLX training is a separate Apple Silicon backend, not torch.to("mlx"). The six-stage MLX trainer mirrors the PyTorch loss stack in chronos.mlx.*: masked CE, DPO/ORPO/GRPO/distillation losses, load balance, temporal locality, lookahead soft-target supervision, lookahead top-k hit loss, and router KL anchor. Numerically sensitive pieces run in FP32 even when model weights use BF16/FP16, which is why auto prefers BF16 on MLX/MPS: FP16 has too little exponent range for router softmax, CE, and Adam moments on small unstable training runs.

The MLX Web UI trainer reports the same fields as CPU/MPS training: step/loss/steps-per-second/ETA, checkpoint-save events, stop events, and verify results. Stop is cooperative at batch boundaries and save intervals write a valid .pth plus sibling .config.json, so MLX stages can resume or feed the PyTorch/export pipeline.

The full six-stage comparison harness lives in tools/compare_minimind_chronos_v3.py.

Backend dispatch (M5)

flowchart TD
    Request["User request<br/>train / inference / WebUI / CLI"] --> Dispatcher["BackendDispatcher"]
    Dispatcher --> Probe["Capability probes"]
    Probe --> CUDA["CUDA<br/>training + inference"]
    Probe --> MPS["MPS<br/>training + inference"]
    Probe --> MLX["MLX<br/>Apple Silicon native path"]
    Probe --> CPU["CPU<br/>portable fallback"]
    Probe --> EXT["Extension hooks<br/>Vulkan / OpenCL"]
    Dispatcher --> Choice["Priority + availability decision"]
    Choice --> Runtime["Chronos runtime / trainer"]
Loading 
from chronos.backend import BackendDispatcher

d = BackendDispatcher()
d.available()   # ['mlx', 'mps', 'cpu'] on Apple Silicon
                # ['cuda', 'cpu']        on NVIDIA hosts
d.select()      # choose the best available backend automatically
d.describe()    # human-readable capability summary
  • First-class backends for training and inference: cpu, mps, cuda, mlx
  • Inference-only / experimental: vulkan when PyTorch was custom-built with USE_VULKAN=ON
  • Third-party extension hook: opencl, via chronos/backend/ext/opencl.py:PROBE()
  • Apple Silicon policy: inference auto still prefers MLX; training keeps MLX on the native chronos.mlx.* path instead of calling torch.model.to("mlx").

Honest note: upstream PyTorch does not ship a real OpenCL backend, and Vulkan support is still niche. Chronos provides a dispatcher seam so external integrations can plug in cleanly without touching core code.

MLX lazy/offload runtime

MLX uses Apple unified memory, so Chronos treats "VRAM" and "RAM" as logical tiers rather than physical buses. The native lazy runtime still enforces the same offload contract as CUDA/MPS/CPU:

  • Hot slots hold only the execution-budget experts materialized as live MLX modules.
  • Warm cache holds a bounded prediction buffer loaded from .ctsr safetensors, never a hidden copy of every expert.
  • Cold storage is the checkpoint/export reader or per-expert cluster cache. In lazy mode, live experts are replaced by placeholders after cache creation; Chronos does not keep a _saved_live full-expert repair cache.
  • Lookahead predictions are queued into the warm cache and only promoted when ready. A true miss synchronously materializes the selected expert only; it does not full-load the model.
  • MLX attention dynamically grows RoPE lookup tables past max_position_embeddings, so long prompts plus decode do not fail at token 257/513/etc.

Inference compare/sweep reports real hot/warm counts, resident hit rate, prediction hit rate, sync SSD loads, MLX active/cache/peak memory, process RSS, prefill/decode time, and tokens/sec. A lazy run is not considered offload-ready unless deterministic exact-lazy output matches full-DRAM output and fallback weight stays zero.

Hugging Face and vLLM compatibility (M5)

  • ChronosForCausalLM subclasses PreTrainedModel and registers AutoConfig and AutoModelForCausalLM, so loading does not require trust_remote_code:

    from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("./out_dir")

  • chronos.model.hf_io.save_chronos_pretrained and load_chronos_pretrained emit standard model.safetensors + config.json, while also carrying cluster_manifest.json and .ctsr files for expert-cache layout. Roundtrip logit drift is 0.00e+00.

  • chronos.serving.register_chronos_with_vllm() registers Chronos with the vLLM ModelRegistry when vLLM is installed. If vLLM is absent, it prints an install hint and exits cleanly. Worker-side mask injection is documented in docs/vllm_integration.md.

Compared with existing offload stacks

Feature llama.cpp offload vLLM offload Project Chronos
Expert prediction None None Predictive (IntentCLF + LookaheadRouter)
Lookahead training n/a n/a Supervised L_lookahead (M2)
IO timing During decode, blocking During decode, blocking During prefill, async
Decode pipeline Synchronous Synchronous Dual-stream + per-expert events (M3)
Cache miss behavior Hard stall Hard stall Soft gating, zero hard stall
Disk format GGUF safetensors Cluster-packed safetensors (.ctsr)
Training integration Post-hoc patch Post-hoc patch Native six-stage stack + router KL anchor
Backend dispatch Compile-time fixed CUDA only cpu / mps / cuda / mlx + extension hooks
Apple Silicon support Partial No Full MLX backend
Hugging Face compatibility GGUF only Yes Yes, with expert-cache metadata
vLLM compatibility n/a Native Optional adapter

Objective

$$L_{\mathrm{total}} = L_{\mathrm{base}} + \lambda_{\mathrm{bal}} L_{\mathrm{aux}} + \lambda_{\mathrm{tmp}} L_{\mathrm{temporal}} + \lambda_{\mathrm{LA}} L_{\mathrm{lookahead}} + \lambda_{\mathrm{anc}} L_{\mathrm{routerKL}}$$ $$L_{\mathrm{aux}} = E \sum_{e=1}^{E} load_e \cdot \overline{p}_e$$ $$L_{\mathrm{temporal}} = \mathbb{E}_{b,t} \left[ \left\| P_{b,t,:} - P_{b,t-1,:} \right\|_2^2 \right]$$ $$L_{\mathrm{routerKL}} = D_{\mathrm{KL}} \left( \pi_{\theta}^{\mathrm{router}} \| \pi_{\mathrm{ref}}^{\mathrm{router}} \right)$$
  • L_base: stage-specific objective (CE, DPO, ORPO, GRPO, or distillation).
  • L_aux: the unscaled MoE load-balance auxiliary term; Chronos applies lambda_bal once in chronos_loss_term.
  • L_temporal: encourages adjacent tokens to reuse similar expert distributions.
  • L_lookahead: soft-target cross entropy from the real future router distribution to the lookahead prediction. sg(...) means stop-gradient.
  • L_routerKL: keeps alignment-stage updates from destroying the routing layout captured at stage start.

All lambda terms are searchable with Optuna TPE, together with structural hyperparameters such as hidden_size, num_experts, and kv_latent_dim.

Installation

pip install Project_Chronos

Or from source:

git clone https://github.com/FonaTech/Project_Chronos
cd Project_Chronos
pip install -e ".[dev]"

MLX (Apple Silicon):

pip install "Project_Chronos[mlx]"

vLLM serving (optional, Linux + CUDA only):

pip install vllm

minimind dependency: Project Chronos uses minimind as its MoE kernel. If it is not found locally, Chronos clones it automatically into ~/.cache/chronos/minimind-master/ on first import. minimind is licensed under Apache-2.0. See THIRD_PARTY_NOTICES.md for attribution details.

Requirements: Python 3.10+, PyTorch 2.4+

Quick start

Web UI (M6: 8 tabs, 4 languages)

chronos-ui
# or
python chronos_app.py

Tabs included:

  • Config with a live parameter / memory estimator merged in from the old Designer
  • Train with its own data_path
  • 6-Stage Pipeline with per-stage dataset paths
  • Inference
  • Export for FP16/Q8_0 safetensors and GGUF deployment artifacts
  • Benchmark with Markdown table + comparison plots
  • Auto-Tune with persistent logs and one-click Apply Best -> Config
  • IO Monitor

Built-in i18n: zh-Hans, zh-Hant, en, ja

Deployment export

chronos export \
    --model_path ./out/sft_384_moe.pth \
    --output_dir ./exports/sft_384 \
    --formats fp16-safetensors q8_0-safetensors fp16-gguf q8_0-gguf

Exports include config.json, chronos_export_manifest.json, and Chronos metadata for MoE top-k, shared fallback experts, lookahead router, hybrid attention, and optional expert-cache layout. Chronos can load exported safetensors/GGUF artifacts through its native lazy expert loader.

Compatibility note: the GGUF files use general.architecture=chronos. Stock Ollama/llama.cpp builds need a Chronos architecture adapter to execute them correctly; Chronos is not a LLaMA tensor-layout clone.

Stage 1: pretrain

python train_chronos.py \
    --data_path ./tests/fixtures/tiny_pretrain.jsonl \
    --hidden_size 256 --num_hidden_layers 4 --num_experts 4 \
    --epochs 1 --device cpu --save_dir ./out

Stage 2-5: alignment chain

python train_chronos_sft.py  --data_path ./tests/fixtures/tiny_sft.jsonl  --from_weight chronos --save_dir ./out --device cpu
python train_chronos_dpo.py  --data_path ./tests/fixtures/tiny_dpo.jsonl  --from_weight sft     --save_dir ./out --device cpu
python train_chronos_orpo.py --data_path ./tests/fixtures/tiny_dpo.jsonl  --from_weight sft     --save_dir ./out --device cpu
python train_chronos_grpo.py --data_path ./tests/fixtures/tiny_grpo.jsonl --from_weight orpo    --save_dir ./out --device cpu \
    --reward toy   # or lm:/path/to/reward-model

Stage 6: distillation

python train_chronos_distill.py \
    --data_path ./tests/fixtures/tiny_sft.jsonl \
    --teacher_path ./out/sft_192_moe.pth \
    --from_weight grpo --save_dir ./out --device cpu \
    --alpha 0.7 --temperature 4.0

Checkpoint and offload diagnostics

Every new .pth checkpoint writes a sibling *.config.json with the MoE topology that cannot be recovered from tensor shapes, including num_experts_per_tok. Use the diagnostic command to verify chat-template generation, no-mask vs all-available masked drift, cold shared fallback, LookaheadRouter prediction quality, and SSD/RAM/VRAM offload stats.

python diagnose_checkpoint.py \
    --model_path ./out/sft_384_moe.pth \
    --config_path ./chronos_config.json \
    --sft_data ./Dataset/sft_t2t.jsonl \
    --mlx_parity \
    --device cpu

# or through the unified CLI:
chronos diagnose --model_path ./out/sft_384_moe.pth --config_path ./chronos_config.json

For backend speed and dtype sanity checks:

python benchmark_training_backends.py --backends cpu mps mlx --dtypes auto bfloat16 float16 --steps 2

End-to-end comparison (minimind vs Chronos)

python tools/compare_minimind_chronos_v3.py \
    --pretrain_steps 150 --align_steps 30 --distill_steps 30 \
    --simulated_ssd_ms 30 --device cpu \
    --output results/compare_results_v3.json

This emits per-stage loss, HF roundtrip logit delta, tokens/sec, active-expert ratio, resident-expert bytes, M3 pipeline slack, and the backend inventory on the current host.

Cluster-pack expert storage for sequential SSD reads

from chronos.io.cluster_layout import (
    collect_activation_log,
    build_cooccurrence_matrix,
    try_louvain_clustering,
    repack_expert_weights_safetensors,
)

log = collect_activation_log(model, calib_loader, "cpu", max_batches=50)
clusters = try_louvain_clustering(build_cooccurrence_matrix(log, num_experts))
repack_expert_weights_safetensors(model, clusters, "./expert_cache_clustered")

Search lambdas and structure hyperparameters automatically

from chronos.tuning.chronos_auto_tuner import ChronosAutoTuner, ChronosSearchSpaceConfig

tuner = ChronosAutoTuner()
tuner.start(
    model_id="./out/chronos_256_moe.pth",
    dataset_path="./dataset/train.jsonl",
    search_space=ChronosSearchSpaceConfig(
        tune_lambda_balance=True,
        tune_lambda_temporal=True,
        tune_lambda_lookahead=True,
        tune_lookahead_steps=True,
        tune_hidden_size=True,
        tune_num_experts=True,
        tune_num_shared_experts=True,
        tune_kv_latent_dim=True,
    ),
    n_trials=20,
)

Project layout

Project_Chronos/
├── chronos/
│   ├── deps.py                    # Auto-download minimind if missing
│   ├── __init__.py                # AutoConfig / AutoModelForCausalLM registration
│   ├── model/
│   │   ├── config.py              # ChronosConfig
│   │   ├── hybrid_attention.py    # MLAAttention + SlidingWindowAttention
│   │   ├── lookahead_router.py    # Per-token lookahead predictor
│   │   ├── moe_chronos.py         # ChronosMOEFeedForward + shared experts + soft gating
│   │   ├── model_chronos.py       # ChronosForCausalLM
│   │   ├── temporal_loss.py       # Temporal locality + lookahead losses
│   │   └── hf_io.py               # save/load_chronos_pretrained + HF registration
│   ├── io/
│   │   ├── expert_store.py        # Three-tier storage + per-expert events
│   │   ├── async_prefetcher.py    # Async prefetch engine
│   │   ├── storage.py             # ClusterStorage: .ctsr safetensors + manifest
│   │   ├── cluster_layout.py      # Co-occurrence clustering + repacking
│   │   └── io_simulator.py        # CHRONOS_SIM_SSD_MS test hook
│   ├── router/
│   │   ├── intent_classifier.py   # Prompt-level expert predictor
│   │   ├── expert_predictor.py    # IntentVector -> ExpertSet
│   │   └── prefill_scheduler.py   # Prefill-time expert preloader
│   ├── mlx/
│   │   ├── attention.py / moe.py / model.py / expert_store.py / inference.py
│   ├── runtime/
│   │   ├── cache_manager.py       # prefetch_for_next_step / ensure_resident
│   │   ├── inference_engine.py    # End-to-end inference engine
│   │   └── metrics.py             # MetricsBus for the IO Monitor
│   ├── trainer/
│   │   ├── loss_mixin.py          # chronos_loss_term + router_kl_anchor
│   │   ├── chronos_trainer.py     # Pretrain
│   │   ├── sft_trainer.py         # Stage 2
│   │   ├── dpo_trainer.py         # Stage 3
│   │   ├── orpo_trainer.py        # Stage 4
│   │   ├── grpo_trainer.py        # Stage 5
│   │   ├── distill_trainer.py     # Stage 6
│   │   └── reward.py              # ToyReward / LMRewardModel / build_reward_fn
│   ├── tuning/
│   │   └── chronos_auto_tuner.py  # Optuna lambda + architecture search
│   ├── eval/
│   │   ├── io_profiler.py         # Lookahead validation
│   │   └── benchmark.py           # End-to-end benchmarking
│   ├── data/
│   │   └── flexible_dataset.py    # Flexible JSONL dataset loader
│   ├── backend/
│   │   ├── __init__.py            # BackendDispatcher (cpu/mps/cuda/mlx)
│   │   ├── dispatcher.py          # Capability probing + priority logic
│   │   └── ext/opencl.py          # Third-party OpenCL extension hook
│   ├── _backend_legacy.py         # Backward-compatible older APIs
│   ├── serving/
│   │   ├── __init__.py
│   │   └── vllm_adapter.py        # Optional vLLM registration
│   └── cli.py                     # Unified CLI
├── ui/                            # Gradio Web UI (zh-Hans / zh-Hant / en / ja)
│   ├── i18n.py
│   ├── estimator.py               # Live parameter / memory estimator
│   └── tabs/
│       ├── config_tab.py          # Config + Designer merged together
│       ├── train_tab.py           # Owns data_path
│       ├── pipeline_tab.py        # Per-stage datasets across all 6 stages
│       ├── inference_tab.py
│       ├── benchmark_tab.py       # Markdown table + gr.BarPlot
│       ├── autotune_tab.py        # Persistent logs + Apply Best -> Config
│       └── iomon_tab.py           # MetricsBus dashboard
├── chronos_app.py                 # Web UI entry
├── train_chronos.py               # Stage 1 entry
├── train_chronos_sft.py           # Stage 2 entry
├── train_chronos_dpo.py           # Stage 3 entry
├── train_chronos_orpo.py          # Stage 4 entry
├── train_chronos_grpo.py          # Stage 5 entry
├── train_chronos_distill.py       # Stage 6 entry
├── tools/
│   ├── compare_minimind_chronos.py
│   ├── compare_minimind_chronos_v2.py
│   └── compare_minimind_chronos_v3.py
├── tests/
│   ├── test_smoke.py
│   ├── test_smoke_cuda.py
│   └── fixtures/
├── docs/
│   └── vllm_integration.md
├── pyproject.toml
└── README.md / README_zh.md / THIRD_PARTY_NOTICES.md

Roadmap

timeline
    title Project Chronos delivery map
    Phase 1
        : LookaheadRouter
        : Temporal locality regularization
        : Router-probability collection path
    Phase 2
        : Async IO engine
        : Three-tier SSD/RAM/VRAM storage
        : Co-activation clustering
    Phase 3
        : Hybrid MLA + SlidingWindow attention
        : PrefillScheduler
        : Dual-layer routing
    Phase 4
        : Native MLX backend
        : Web UI and CLI
        : Optuna search
        : Open-source release
    M1-M3
        : Cluster-aware safetensors storage
        : Supervised lookahead loss
        : Dual-stream decode with per-expert events
    M4-M6
        : SFT / DPO / ORPO / GRPO trainers
        : Router KL anchor
        : HF IO, vLLM adapter, multi-backend dispatch
        : Stage 6 distillation and pluggable rewards
        : Web UI v2, benchmark plots, IO Monitor
    Next
        : Train IntentClassifier on large activation corpora
        : Benchmark 7B+ checkpoints
        : Inject masks on the vLLM worker path
        : Ship real Vulkan / OpenCL kernels
Loading 
mindmap
  root((Chronos innovation surface))
    Predictive routing
      IntentClassifier
        Prompt-level hot expert prior
        Budgeted expert-set prediction
      LookaheadRouter
        Per-token future routing
        Soft-target CE from real future routers
    Storage-aware MoE
      Clustered safetensors
        .ctsr packed expert clusters
        manifest-driven mmap
      Three-tier cache
        NVMe SSD
        Pinned RAM
        VRAM / unified memory
      Soft fallback
        Shared experts always resident
        No hard stall on cache miss
    Decode pipeline
      Prefill-time expert loading
      AsyncPrefetcher queue
      H2D stream
      Per-expert CUDA events
    Training stack
      Pretrain
      SFT
      DPO
      ORPO
      GRPO
      Distill
      Router KL anchor
    Deployment
      HF safetensors IO
      AutoModel registration
      vLLM adapter
      CPU / CUDA / MPS / MLX dispatch
      Web UI and CLI
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Citation

@misc{chronos2026,
  title  = {Project Chronos: Prefill-Time Expert Loading and Dual-Layer Routing
             for Zero-Stall On-Device MoE Inference},
  author = {Fona and Project Chronos Contributors},
  year   = {2026},
  url    = {https://github.com/FonaTech/Project_Chronos}
}

Third-party attribution

Project Chronos builds on jingyaogong's minimind, licensed under Apache-2.0. Full attribution lives in THIRD_PARTY_NOTICES.md.

License

Apache 2.0 - see LICENSE