if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
# ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
res = "hunyuan"
+ if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
+ # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
+ res = "hunyuan-dense"
if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
# ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
res = "falcon-h1"
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)
_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'")
raise ValueError(f"Unprocessed experts: {experts}")
+@ModelBase.register("HunYuanDenseV1ForCausalLM")
+class HunYuanModel(TextModel):
+ model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
+
+ def set_vocab(self):
+ if (self.dir_model / "tokenizer.json").is_file():
+ self._set_vocab_gpt2()
+ else:
+ 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:
+ 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
+ if self.hparams['hidden_size'] == 4096:
+ self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
+
+ def set_gguf_parameters(self):
+ super().set_gguf_parameters()
+ hparams = self.hparams
+
+ # 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", 50)
+ base = hparams.get("rope_theta", 10000.0)
+ dim = hparams["head_dim"]
+ scaled_base = base * (alpha ** (dim / (dim - 2)))
+ 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 base == 10000.0 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"
+
+ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ if name == "lm_head.weight":
+ if self.hparams.get("tie_word_embeddings", False):
+ logger.info("Skipping tied output layer 'lm_head.weight'")
+ return []
+
+ return [(self.map_tensor_name(name), data_torch)]
+
+
@ModelBase.register("SmolLM3ForCausalLM")
class SmolLM3Model(LlamaModel):
model_arch = gguf.MODEL_ARCH.SMOLLM3
{"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"},
+ {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
# falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
ERNIE4_5 = auto()
ERNIE4_5_MOE = auto()
HUNYUAN_MOE = auto()
+ HUNYUAN_DENSE = auto()
SMOLLM3 = auto()
LFM2 = auto()
DREAM = auto()
MODEL_ARCH.ERNIE4_5_MOE: "ernie4_5-moe",
MODEL_ARCH.FALCON_H1: "falcon-h1",
MODEL_ARCH.HUNYUAN_MOE: "hunyuan-moe",
+ MODEL_ARCH.HUNYUAN_DENSE: "hunyuan-dense",
MODEL_ARCH.SMOLLM3: "smollm3",
MODEL_ARCH.LFM2: "lfm2",
MODEL_ARCH.DREAM: "dream",
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
+ MODEL_ARCH.HUNYUAN_DENSE: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT_NORM,
+ MODEL_TENSOR.OUTPUT,
+ 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_NORM,
+ MODEL_TENSOR.FFN_GATE,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_UP,
+ ],
MODEL_ARCH.SMOLLM3: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
{ LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" },
{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
+ { LLM_ARCH_HUNYUAN_DENSE, "hunyuan-dense" },
{ LLM_ARCH_SMOLLM3, "smollm3" },
{ LLM_ARCH_LFM2, "lfm2" },
{ LLM_ARCH_DREAM, "dream" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
+ {
+ LLM_ARCH_HUNYUAN_DENSE,
+ {
+ { 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_NORM, "blk.%d.ffn_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_SMOLLM3,
{
LLM_ARCH_ERNIE4_5,
LLM_ARCH_ERNIE4_5_MOE,
LLM_ARCH_HUNYUAN_MOE,
+ LLM_ARCH_HUNYUAN_DENSE,
LLM_ARCH_SMOLLM3,
LLM_ARCH_LFM2,
LLM_ARCH_DREAM,
{ "llama4", LLM_CHAT_TEMPLATE_LLAMA4 },
{ "smolvlm", LLM_CHAT_TEMPLATE_SMOLVLM },
{ "hunyuan-moe", LLM_CHAT_TEMPLATE_HUNYUAN_MOE },
+ { "hunyuan-dense", LLM_CHAT_TEMPLATE_HUNYUAN_DENSE },
{ "kimi-k2", LLM_CHAT_TEMPLATE_KIMI_K2 },
};
return LLM_CHAT_TEMPLATE_DOTS1;
} else if (tmpl_contains("<|startoftext|>") && tmpl_contains("<|extra_4|>")) {
return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
+ } else if (tmpl_contains("<｜hy_place▁holder▁no▁2｜>") && tmpl_contains("<｜hy_place▁holder▁no▁3｜>")) {
+ return LLM_CHAT_TEMPLATE_HUNYUAN_DENSE;
} else if (tmpl_contains("<|im_assistant|>assistant<|im_middle|>")) {
return LLM_CHAT_TEMPLATE_KIMI_K2;
}
if (role == "system") {
ss << "<|startoftext|>" << message->content << "<|extra_4|>";
} else if (role == "assistant") {
- ss << "<|startoftext|>" << message->content << "<|eos|>";
+ ss << message->content << "<|eos|>";
} else {
ss << "<|startoftext|>" << message->content << "<|extra_0|>";
}
}
+ } else if (tmpl == LLM_CHAT_TEMPLATE_HUNYUAN_DENSE) {
+ // tencent/Hunyuan-4B-Instruct
+ for (size_t i = 0; i < chat.size(); i++) {
+ std::string role(chat[i]->role);
+ if (i == 0) {
+ if (role == "system") {
+ ss << chat[i]->content << "<｜hy_place▁holder▁no▁3｜>";
+ }
+ }
+
+ if (role == "assistant") {
+ ss << "<｜hy_Assistant｜>" << chat[i]->content << "<｜hy_place▁holder▁no▁2｜>";
+ } else if (role == "user") {
+ ss << "<｜hy_User｜>" << chat[i]->content << "<｜hy_Assistant｜>";
+ }
+ }
} else if (tmpl == LLM_CHAT_TEMPLATE_KIMI_K2) {
// moonshotai/Kimi-K2-Instruct
for (auto message : chat) {
LLM_CHAT_TEMPLATE_SMOLVLM,
LLM_CHAT_TEMPLATE_DOTS1,
LLM_CHAT_TEMPLATE_HUNYUAN_MOE,
+ LLM_CHAT_TEMPLATE_HUNYUAN_DENSE,
LLM_CHAT_TEMPLATE_KIMI_K2,
LLM_CHAT_TEMPLATE_UNKNOWN,
};
default: type = LLM_TYPE_UNKNOWN;
}
} break;
+ case LLM_ARCH_HUNYUAN_DENSE:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_embd) {
+ case 1024: type = LLM_TYPE_0_5B; break;
+ case 2048: type = LLM_TYPE_1_8B; break;
+ case 3072: type = LLM_TYPE_4B; break;
+ case 4096: type = LLM_TYPE_7B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+ } break;
case LLM_ARCH_SMOLLM3:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
}
} break;
+ case LLM_ARCH_HUNYUAN_DENSE:
+ {
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+ // output
+ 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 is NULL, init from the input tok embed
+ 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];
+
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+ layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+
+ }
+ } break;
case LLM_ARCH_SMOLLM3:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
}
};
+struct llm_build_hunyuan_dense : public llm_graph_context {
+ llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv_unified();
+
+ const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = build_norm(inpL,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+ // self-attention
+ {
+ // rope freq factors for llama3; may return nullptr for llama2 and other models
+ ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+ // compute Q and K and RoPE them
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ Kcur = build_norm(Kcur,
+ model.layers[il].attn_k_norm, nullptr,
+ LLM_NORM_RMS, il);
+ cb(Kcur, "Kcur_norm", il);
+
+ Qcur = build_norm(Qcur,
+ model.layers[il].attn_q_norm, nullptr,
+ LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_norm", il);
+
+ cur = build_attn(inp_attn,
+ model.layers[il].wo, model.layers[il].bo,
+ Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
+ cb(cur, "attn_out", il);
+ }
+
+ if (il == n_layer - 1 && inp_out_ids) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+ // feed-forward network (non-MoE)
+ ggml_tensor * cur_mlp = build_ffn(cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur_mlp, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur_mlp, ffn_inp);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+ cur = inpL;
+
+ cur = build_norm(cur,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, -1);
+
+ cb(cur, "result_norm", -1);
+ res->t_embd = cur;
+ // lm_head
+ cur = build_lora_mm(model.output, cur);
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+ }
+};
+
struct llm_build_smollm3 : public llm_graph_context {
llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
{
llm = std::make_unique<llm_build_hunyuan_moe>(*this, params);
} break;
+ case LLM_ARCH_HUNYUAN_DENSE:
+ {
+ llm = std::make_unique<llm_build_hunyuan_dense>(*this, params);
+ } break;
case LLM_ARCH_SMOLLM3:
{
llm = std::make_unique<llm_build_smollm3>(*this, params);
case LLM_ARCH_MINICPM3:
case LLM_ARCH_DOTS1:
case LLM_ARCH_HUNYUAN_MOE:
+ case LLM_ARCH_HUNYUAN_DENSE:
case LLM_ARCH_LFM2:
case LLM_ARCH_SMALLTHINKER:
return LLAMA_ROPE_TYPE_NEOX;
};
break;
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
+ case LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE:
regex_exprs = {
"\\p{N}{1,3}",
"[一-龥-ゟ゠-ヿ]+",
tokenizer_pre == "hunyuan") {
pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN;
clean_spaces = false;
+ } else if (
+ tokenizer_pre == "hunyuan-dense") {
+ pre_type = LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE;
+ clean_spaces = false;
} else if (
tokenizer_pre == "kimi-k2") {
pre_type = LLAMA_VOCAB_PRE_TYPE_KIMI_K2;
LLAMA_VOCAB_PRE_TYPE_SEED_CODER = 35,
LLAMA_VOCAB_PRE_TYPE_HUNYUAN = 36,
LLAMA_VOCAB_PRE_TYPE_KIMI_K2 = 37,
+ LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE = 38,
};
struct LLM_KV;
overview / pkg / ggml / sources / llama.cpp / commitdiff