[model] Add support for Plamo3

convert_hf_to_gguf.py

@@ -4892,6 +4892,157 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return [(new_name, data_torch)]
 
 
+@ModelBase.register("Plamo3ForCausalLM", "PLaMo3ForCausalLM")
+class Plamo3Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.PLAMO3
+
+    def set_vocab(self):
+        # PLaMo models use a custom tokenizer with a .jsonl file
+        tokenizer_jsonl_path = self.dir_model / "tokenizer.jsonl"
+        tokenizer_config_path = self.dir_model / "tokenizer_config.json"
+
+        if not tokenizer_jsonl_path.is_file():
+            raise FileNotFoundError(f"PLaMo tokenizer file not found: {tokenizer_jsonl_path}")
+
+        # Load tokenizer config
+        with open(tokenizer_config_path, "r", encoding="utf-8") as f:
+            tokenizer_config = json.load(f)
+
+        # Load tokens from JSONL file (actually a list format)
+        tokens = []
+        scores = []
+        toktypes = []
+
+        with open(tokenizer_jsonl_path, "r", encoding="utf-8") as f:
+            for line_num, line in enumerate(f):
+                if line.strip():
+                    token_data = json.loads(line)
+                    # Format: [token, score, type, ?, ?, ?, ?]
+                    token = token_data[0].encode("utf-8")
+                    score = float(token_data[1])
+                    token_type_str = token_data[2] if len(token_data) > 2 else "NORMAL"
+
+                    tokens.append(token)
+                    scores.append(score)
+
+                    if token_type_str == "UNKNOWN":
+                        toktypes.append(gguf.TokenType.UNKNOWN)
+                    elif token_type_str == "CONTROL":
+                        toktypes.append(gguf.TokenType.CONTROL)
+                    elif token_type_str == "BYTE":
+                        toktypes.append(gguf.TokenType.BYTE)
+                    else:
+                        token_str = token_data[0]
+                        if token_str.startswith("<|plamo:") and token_str.endswith("|>"):
+                            toktypes.append(gguf.TokenType.CONTROL)
+                        else:
+                            toktypes.append(gguf.TokenType.NORMAL)
+
+        vocab_size = self.hparams["vocab_size"]
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(gguf.TokenType.UNUSED)
+
+        self.gguf_writer.add_tokenizer_model("plamo2")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] is not None:
+            token_id = tokens.index(tokenizer_config["bos_token"].encode("utf-8"))
+            self.gguf_writer.add_bos_token_id(token_id)
+        if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] is not None:
+            token_id = tokens.index(tokenizer_config["eos_token"].encode("utf-8"))
+            self.gguf_writer.add_eos_token_id(token_id)
+        if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] is not None:
+            token_id = tokens.index(tokenizer_config["pad_token"].encode("utf-8"))
+            self.gguf_writer.add_pad_token_id(token_id)
+        if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] is not None:
+            token_id = tokens.index(tokenizer_config["sep_token"].encode("utf-8"))
+            self.gguf_writer.add_sep_token_id(token_id)
+        if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] is not None:
+            token_id = tokens.index(tokenizer_config["unk_token"].encode("utf-8"))
+            self.gguf_writer.add_unk_token_id(token_id)
+
+        # Add <|plamo:op|> as EOT to ensure appropriate end of generation
+        self.gguf_writer.add_eot_token_id(4)
+
+        self.gguf_writer.add_add_space_prefix(False)
+
+        if "chat_template" in tokenizer_config and tokenizer_config["chat_template"] is not None:
+            self.gguf_writer.add_chat_template(tokenizer_config["chat_template"])
+
+    def _sliding_window_pattern(self, block_count: int) -> list[bool]:
+        layer_types = self.hparams.get("layer_types")
+        if isinstance(layer_types, list) and len(layer_types) == block_count:
+            return [t == "sliding_attention" for t in layer_types]
+
+        pattern = self.hparams.get("sliding_window_pattern")
+        if isinstance(pattern, int) and pattern > 0:
+            return [((i + 1) % pattern) != 0 for i in range(block_count)]
+
+        return []
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        block_count = hparams["num_hidden_layers"]
+
+        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
+        self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_head_count(hparams["num_attention_heads"])
+        self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
+        head_dim = hparams["head_dim"]
+        self.gguf_writer.add_key_length(head_dim)
+        self.gguf_writer.add_value_length(head_dim)
+        self.gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
+        self.gguf_writer.add_rope_freq_base(hparams["rope_theta"])
+
+        window_size = hparams.get("window_size") or hparams.get("sliding_window") or 0
+        self.gguf_writer.add_sliding_window(window_size)
+
+        pattern = self._sliding_window_pattern(block_count)
+        if len(pattern) == block_count and any(pattern):
+            self.gguf_writer.add_sliding_window_pattern(pattern)
+
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+
+        if name.endswith(".pre_mixer_norm.weight"):
+            data_torch = data_torch + 1.0
+        elif name.endswith(".post_mixer_norm.weight"):
+            data_torch = data_torch + 1.0 / 5
+        elif name.endswith(".pre_mlp_norm.weight"):
+            data_torch = data_torch + 1.0
+        elif name.endswith(".post_mlp_norm.weight"):
+            data_torch = data_torch + 1.0 / (5**1.5)
+        elif name.endswith((".mixer.q_norm.weight", ".mixer.k_norm.weight")):
+            data_torch = data_torch + 1.0
+        elif name.endswith(".norm.weight"):
+            data_torch = data_torch + 1.0
+
+        results: list[tuple[str, Tensor]] = []
+
+        if "gate_up_proj.weight" in name:
+            name_up = name.replace("gate_up_proj.weight", "up_proj.weight")
+            name_gate = name.replace("gate_up_proj.weight", "gate_proj.weight")
+            gate_proj_weight, up_proj_weight = torch.chunk(data_torch, 2, dim=0)
+            results.append((self.map_tensor_name(name_gate), gate_proj_weight))
+            results.append((self.map_tensor_name(name_up), up_proj_weight))
+        else:
+            results.append((self.map_tensor_name(name), data_torch))
+
+        return results
+
+
 @ModelBase.register("CodeShellForCausalLM")
 class CodeShellModel(TextModel):
     model_arch = gguf.MODEL_ARCH.CODESHELL

gguf-py/gguf/constants.py

@@ -359,6 +359,7 @@ class MODEL_ARCH(IntEnum):
     PHIMOE           = auto()
     PLAMO            = auto()
     PLAMO2           = auto()
+    PLAMO3           = auto()
     CODESHELL        = auto()
     ORION            = auto()
     INTERNLM2        = auto()
@@ -728,6 +729,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.PHIMOE:           "phimoe",
     MODEL_ARCH.PLAMO:            "plamo",
     MODEL_ARCH.PLAMO2:           "plamo2",
+    MODEL_ARCH.PLAMO3:           "plamo3",
     MODEL_ARCH.CODESHELL:        "codeshell",
     MODEL_ARCH.ORION:            "orion",
     MODEL_ARCH.INTERNLM2:        "internlm2",
@@ -1632,6 +1634,23 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.SSM_B_NORM,
         MODEL_TENSOR.SSM_C_NORM,
     ],
+    MODEL_ARCH.PLAMO3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_POST_NORM,
+    ],
     MODEL_ARCH.GPT2: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.POS_EMBD,

gguf-py/gguf/tensor_mapping.py

@@ -574,6 +574,7 @@ class TensorNameMap:
             "encoder.layer.{bid}.attention.self.layer_norm_q",                # jina-bert-v2
             "transformer.layers.{bid}.attn.q_norm",                           # openelm
             "model.layers.layers.{bid}.mixer.q",                              # plamo2
+            "model.layers.layers.{bid}.mixer.q_norm",                         # plamo3
             "layers.{bid}.self_attn.q_norm",                                  # qwen3-embedding
             "model.layers.{bid}.attention.query_layernorm",                   # apertus
         ),
@@ -589,6 +590,7 @@ class TensorNameMap:
             "encoder.layer.{bid}.attention.self.layer_norm_k",                # jina-bert-v2
             "transformer.layers.{bid}.attn.k_norm",                           # openelm
             "model.layers.layers.{bid}.mixer.k",                              # plamo2
+            "model.layers.layers.{bid}.mixer.k_norm",                         # plamo3
             "layers.{bid}.self_attn.k_norm",                                  # qwen3-embedding
             "model.layers.{bid}.attention.key_layernorm",                     # apertus
         ),

src/CMakeLists.txt

@@ -105,6 +105,7 @@ add_library(llama
             models/phi3.cpp
             models/plamo.cpp
             models/plamo2.cpp
+            models/plamo3.cpp
             models/plm.cpp
             models/qwen.cpp
             models/qwen2.cpp

src/llama-arch.cpp

@@ -39,6 +39,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_PHIMOE,           "phimoe"           },
     { LLM_ARCH_PLAMO,            "plamo"            },
     { LLM_ARCH_PLAMO2,           "plamo2"           },
+    { LLM_ARCH_PLAMO3,           "plamo3"           },
     { LLM_ARCH_CODESHELL,        "codeshell"        },
     { LLM_ARCH_ORION,            "orion"            },
     { LLM_ARCH_INTERNLM2,        "internlm2"        },
@@ -959,6 +960,26 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
         },
     },
+    {
+        LLM_ARCH_PLAMO3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_CODESHELL,
         {

src/llama-arch.h

@@ -43,6 +43,7 @@ enum llm_arch {
     LLM_ARCH_PHIMOE,
     LLM_ARCH_PLAMO,
     LLM_ARCH_PLAMO2,
+    LLM_ARCH_PLAMO3,
     LLM_ARCH_CODESHELL,
     LLM_ARCH_ORION,
     LLM_ARCH_INTERNLM2,

src/llama-model.cpp

@@ -1178,6 +1178,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH,   hparams.n_embd_head_k, false);
                 ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
             } break;
+        case LLM_ARCH_PLAMO3:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+                if (hparams.n_swa == 0) {
+                    hparams.n_swa = 2048;
+                }
+                hparams.set_swa_pattern(8);
+
+                switch (hparams.n_layer) {
+                    case 24: type = LLM_TYPE_2B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_GPT2:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -3650,6 +3665,45 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
                     }
                 } break;
+            case LLM_ARCH_PLAMO3:
+                {
+                    const int64_t head_dim_q = hparams.n_embd_head_k;
+                    const int64_t head_dim_v = hparams.n_embd_head_v;
+
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        const int64_t num_attention_heads = hparams.n_head(i);
+                        const int64_t num_key_value_heads = hparams.n_head_kv(i);
+                        const int64_t q_proj_dim = num_attention_heads * head_dim_q;
+                        const int64_t k_proj_dim = num_key_value_heads * head_dim_q;
+                        const int64_t v_proj_dim = num_key_value_heads * head_dim_v;
+                        const int64_t n_ff_cur   = hparams.n_ff(i);
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i),
+                                {n_embd,q_proj_dim + k_proj_dim + v_proj_dim}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim_q}, 0);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim_q}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {num_attention_heads * head_dim_v, n_embd}, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, i), {n_embd}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, i), {n_embd}, 0);
+
+                        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff_cur}, 0);
+                        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff_cur}, 0);
+                        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff_cur, n_embd}, 0);
+                    }
+                } break;
             case LLM_ARCH_GPT2:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -7118,6 +7172,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_plamo2>(*this, params);
             } break;
+        case LLM_ARCH_PLAMO3:
+            {
+                llm = std::make_unique<llm_build_plamo3>(*this, params);
+            } break;
         case LLM_ARCH_GPT2:
             {
                 llm = std::make_unique<llm_build_gpt2>(*this, params);
@@ -7600,6 +7658,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_PHIMOE:
         case LLM_ARCH_PLAMO:
         case LLM_ARCH_PLAMO2:
+        case LLM_ARCH_PLAMO3:
         case LLM_ARCH_GEMMA:
         case LLM_ARCH_GEMMA2:
         case LLM_ARCH_GEMMA3:

src/models/models.h

@@ -390,6 +390,10 @@ struct llm_build_plamo : public llm_graph_context {
     llm_build_plamo(const llama_model & model, const llm_graph_params & params);
 };
 
+struct llm_build_plamo3 : public llm_graph_context {
+    llm_build_plamo3(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_plm : public llm_graph_context {
     llm_build_plm(const llama_model & model, const llm_graph_params & params);
 };