model : add hunyuan moe

convert_hf_to_gguf.py

@@ -815,6 +815,9 @@ def get_vocab_base_pre(self, tokenizer) -> str:
         if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
             # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
             res = "minerva-7b"
+        if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
+            # ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
+            res = "hunyuan"
 
         if res is None:
             logger.warning("\n")
@@ -6436,6 +6439,155 @@ def set_gguf_parameters(self):
         super().set_gguf_parameters()
         self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
 
+
+@ModelBase.register("HunYuanMoEV1ForCausalLM")
+class HunYuanMoEModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # For handling tied embeddings
+        self._tok_embd = None
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+
+        # 1. Get the pre-tokenizer identifier hash
+        tokpre = self.get_vocab_base_pre(tokenizer)
+
+        # 2. Reverse-engineer the merges list from mergeable_ranks
+        merges = []
+        vocab = {}
+        mergeable_ranks = tokenizer.mergeable_ranks
+        for token, rank in mergeable_ranks.items():
+            vocab[QwenModel.token_bytes_to_string(token)] = rank
+            if len(token) == 1:
+                continue
+            merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
+            if len(merged) == 2: # todo this is an assert in Qwen, why?
+                merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
+
+        # 3. Generate the tokens and toktypes lists
+        vocab_size = self.hparams["vocab_size"]
+        assert tokenizer.vocab_size == vocab_size
+        special_tokens = tokenizer.special_tokens
+        reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
+        tokens: list[str] = []
+        toktypes: list[int] = []
+        for i in range(vocab_size):
+            if i not in reverse_vocab:
+                tokens.append(f"[PAD{i}]")
+                toktypes.append(gguf.TokenType.UNUSED)
+            else:
+                token = reverse_vocab[i]
+                tokens.append(token)
+                if i in special_tokens.values():
+                    toktypes.append(gguf.TokenType.CONTROL)
+                else:
+                    toktypes.append(gguf.TokenType.NORMAL)
+
+        # 4. Write all vocab-related fields to the GGUF writer
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_token_merges(merges)
+
+        # 5. Add special tokens and chat templates
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
+        special_vocab.add_to_gguf(self.gguf_writer)
+        # FIX for BOS token: Overwrite incorrect id read from config.json
+        self.gguf_writer.add_bos_token_id(127959) # <|bos|>
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        self.gguf_writer.add_expert_count(hparams["num_experts"])
+        self.gguf_writer.add_expert_shared_feed_forward_length(hparams["intermediate_size"])
+
+        moe_intermediate_size = hparams["moe_intermediate_size"]
+        assert all(n == moe_intermediate_size[0] for n in moe_intermediate_size)
+        self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size[0])
+
+        moe_topk = hparams["moe_topk"]
+        assert all(topk == moe_topk[0] for topk in moe_topk)
+        self.gguf_writer.add_expert_used_count(moe_topk[0])
+
+        moe_shared_expert = hparams["num_shared_expert"]
+        assert all(n == moe_shared_expert[0] for n in moe_shared_expert)
+        self.gguf_writer.add_expert_shared_count(moe_shared_expert[0])
+
+        # Rope
+        rope_scaling = hparams.get("rope_scaling", {})
+        if rope_scaling.get("type") == "dynamic":
+            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
+            alpha = rope_scaling.get("alpha", 1000)
+            base = hparams.get("rope_theta", 10000.0)
+            dim = (hparams["hidden_size"] // hparams["num_attention_heads"]) # 128
+            scaled_base = base * (alpha ** (dim / (dim - 2))) # 10000 * (1000 ** (128 / 126)) = 11158839.9251
+            self.gguf_writer.add_rope_freq_base(scaled_base)
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
+            self.gguf_writer.add_rope_scaling_factor(1)
+            # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
+            self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
+            self.gguf_writer.add_context_length(256 * 1024) # 256k context length
+
+            # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
+            assert alpha == 1000 and base == 10000.0 and dim == 128 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
+                "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "model.embed_tokens.weight":
+            self._tok_embd = data_torch.clone()
+
+        if name == "lm_head.weight":
+            if self.hparams.get("tie_word_embeddings", False):
+                logger.info("Skipping tied output layer 'lm_head.weight'")
+                return []
+
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                tensors: list[tuple[str, Tensor]] = []
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+                    new_name = self.map_tensor_name(merged_name)
+                    tensors.append((new_name, data_torch))
+
+                return tensors
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
 ###### CONVERSION LOGIC ######
 
 

convert_hf_to_gguf_update.py

@@ -137,6 +137,7 @@ class TOKENIZER_TYPE(IntEnum):
     {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
     {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
     {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
+    {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
 ]
 
 

gguf-py/gguf/constants.py

@@ -355,6 +355,7 @@ class MODEL_ARCH(IntEnum):
     DOTS1            = auto()
     ARCEE            = auto()
     ERNIE4_5         = auto()
+    HUNYUAN_MOE      = auto()
 
 
 class VISION_PROJECTOR_TYPE(IntEnum):
@@ -656,6 +657,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.DOTS1:            "dots1",
     MODEL_ARCH.ARCEE:            "arcee",
     MODEL_ARCH.ERNIE4_5:         "ernie4_5",
+    MODEL_ARCH.HUNYUAN_MOE:      "hunyuan-moe",
 }
 
 VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@@ -2193,6 +2195,27 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.HUNYUAN_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+    ],
     # TODO
 }
 

gguf-py/gguf/tensor_mapping.py

@@ -303,6 +303,7 @@ class TensorNameMap:
             "model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe
             "model.layers.{bid}.feed_forward.router",           # llama4
             "encoder.layers.{bid}.mlp.router.layer",            # nomic-bert-moe
+            "model.layers.{bid}.mlp.gate.wg",                   # hunyuan
         ),
 
         MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -362,6 +363,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_expert.up_proj",          # qwen2moe
             "model.layers.{bid}.mlp.shared_experts.up_proj",         # deepseek deepseek2
             "model.layers.{bid}.feed_forward.shared_expert.up_proj", # llama4
+            "model.layers.{bid}.mlp.shared_mlp.up_proj",             # hunyuan
         ),
 
         # AWQ-activation gate
@@ -398,6 +400,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_expert.gate_proj",          # qwen2moe
             "model.layers.{bid}.mlp.shared_experts.gate_proj",         # deepseek deepseek2
             "model.layers.{bid}.feed_forward.shared_expert.gate_proj", # llama4
+            "model.layers.{bid}.mlp.shared_mlp.gate_proj",             # hunyuan
         ),
 
         # Feed-forward down
@@ -447,11 +450,13 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.shared_experts.down_proj",         # deepseek deepseek2
             "model.layers.{bid}.feed_forward.shared_expert.down_proj", # llama4
             "model.layers.{bid}.shared_mlp.output_linear",             # granitemoe
+            "model.layers.{bid}.mlp.shared_mlp.down_proj",             # hunyuan
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.q_layernorm",
             "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon
+            "model.layers.{bid}.self_attn.query_layernorm",                   # hunyuan
             "model.layers.{bid}.self_attn.q_norm",                            # cohere olmoe chameleon olmo2
             "transformer.blocks.{bid}.attn.q_ln",                             # sea-lion
             "encoder.layer.{bid}.attention.self.layer_norm_q",                # jina-bert-v2
@@ -461,6 +466,7 @@ class TensorNameMap:
         MODEL_TENSOR.ATTN_K_NORM: (
             "language_model.encoder.layers.{bid}.self_attention.k_layernorm",
             "model.layers.{bid}.self_attn.k_layernorm",                       # persimmon
+            "model.layers.{bid}.self_attn.key_layernorm",                     # hunyuan
             "model.layers.{bid}.self_attn.k_norm",                            # cohere olmoe chameleon olmo2
             "transformer.blocks.{bid}.attn.k_ln",                             # sea-lion
             "encoder.layer.{bid}.attention.self.layer_norm_k",                # jina-bert-v2

include/llama.h

@@ -117,6 +117,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_LLAMA4         = 33,
         LLAMA_VOCAB_PRE_TYPE_PIXTRAL        = 34,
         LLAMA_VOCAB_PRE_TYPE_SEED_CODER     = 35,
+        LLAMA_VOCAB_PRE_TYPE_HUNYUAN        = 36,
     };
 
     enum llama_rope_type {

src/llama-arch.cpp

@@ -77,6 +77,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_DOTS1,            "dots1"            },
     { LLM_ARCH_ARCEE,            "arcee"            },
     { LLM_ARCH_ERNIE4_5,         "ernie4_5"         },
+    { LLM_ARCH_HUNYUAN_MOE,      "hunyuan-moe"      },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -1676,6 +1677,29 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_HUNYUAN_MOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,  "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {

src/llama-arch.h

@@ -81,6 +81,7 @@ enum llm_arch {
     LLM_ARCH_DOTS1,
     LLM_ARCH_ARCEE,
     LLM_ARCH_ERNIE4_5,
+    LLM_ARCH_HUNYUAN_MOE,
     LLM_ARCH_UNKNOWN,
 };
 

src/llama-chat.cpp

@@ -64,6 +64,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
     { "bailing",           LLM_CHAT_TEMPLATE_BAILING           },
     { "llama4",            LLM_CHAT_TEMPLATE_LLAMA4            },
     { "smolvlm",           LLM_CHAT_TEMPLATE_SMOLVLM           },
+    { "hunyuan-moe",       LLM_CHAT_TEMPLATE_HUNYUAN_MOE       },
 };
 
 llm_chat_template llm_chat_template_from_str(const std::string & name) {
@@ -185,6 +186,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
         return LLM_CHAT_TEMPLATE_LLAMA4;
     } else if (tmpl_contains("<|endofuserprompt|>")) {
         return LLM_CHAT_TEMPLATE_DOTS1;
+    } else if (tmpl_contains("<|startoftext|>") && tmpl_contains("<|extra_4|>")) {
+        return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
     }
     return LLM_CHAT_TEMPLATE_UNKNOWN;
 }
@@ -665,6 +668,21 @@ int32_t llm_chat_apply_template(
         if (add_ass) {
             ss << "<|response|>";
         }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_HUNYUAN_MOE) {
+        // tencent/Hunyuan-A13B-Instruct
+        for (auto message : chat) {
+            std::string role(message->role);
+            if (role == "system") {
+                ss << "<|startoftext|>" << message->content << "<|extra_4|>";
+            } else if (role == "assistant") {
+                ss << "<|startoftext|>" << message->content << "<|eos|>";
+            } else {
+                ss << "<|startoftext|>" << message->content << "<|extra_0|>";
+            }
+        }
+        if (add_ass) {
+            ss << "<|startoftext|>";
+        }
     } else {
         // template not supported
         return -1;

src/llama-chat.h

@@ -44,6 +44,7 @@ enum llm_chat_template {
     LLM_CHAT_TEMPLATE_LLAMA4,
     LLM_CHAT_TEMPLATE_SMOLVLM,
     LLM_CHAT_TEMPLATE_DOTS1,
+    LLM_CHAT_TEMPLATE_HUNYUAN_MOE,
     LLM_CHAT_TEMPLATE_UNKNOWN,
 };