vbatts/llama.cpp
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llama : Add support for DeepSeek V3 (#11049)

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* convert : extend DEEPSEEK2 model architecture to support DeepseekV3ForCausalLM by adding EXPERT_WEIGHTS_NORM and EXPERT_GATING_FUNC model parameters and FFN_EXP_PROBS_B tensor type

* vocab : add DeepSeek V3 pre-tokenizer regexes

* unicode : handle ACCENT_MARK and SYMBOL categories in regex

* llama : add DeepSeek V3 chat template, handle new model parameters and tensor types

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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
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fairydreaming 2025-01-04 21:06:11 +01:00 • committed by GitHub
parent f922a9c542
commit 9394bbd484
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16 changed files with 162 additions and 5 deletions
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23
convert_hf_to_gguf.py
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@ -687,6 +687,9 @@ class Model:
if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
# ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
res = "megrez"
if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
# ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
res = "deepseek-v3"
if res is None:
logger.warning("\n")
@ -3849,6 +3852,7 @@ class DeepseekModel(Model):
@Model.register("DeepseekV2ForCausalLM")
@Model.register("DeepseekV3ForCausalLM")
class DeepseekV2Model(Model):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
@ -3870,6 +3874,15 @@ class DeepseekV2Model(Model):
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
if hparams["scoring_func"] == "sigmoid":
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
elif hparams["scoring_func"] == "softmax":
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
else:
raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}")
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
@ -3882,6 +3895,16 @@ class DeepseekV2Model(Model):
_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# rename e_score_correction_bias tensors
if name.endswith("e_score_correction_bias"):
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
# skip Multi-Token Prediction (MTP) layers
block_count = self.hparams["num_hidden_layers"]
match = re.match(r"model.layers.(\d+)", name)
if match and int(match.group(1)) >= block_count:
return []
# process the experts separately
if name.find("mlp.experts") != -1:
n_experts = self.hparams["n_routed_experts"]