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model : add text-only support for Kimi-VL (and find special tokens in text_config) (#15051)

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* basic kimi-vl textmodel conversion

* check config["text_config"] for special tokens
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Gabriel Larson 2025-08-03 09:56:25 -05:00 • committed by GitHub
parent 6c7a441161
commit 83bc2f288c
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2 changed files with 13 additions and 1 deletions
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8
convert_hf_to_gguf.py
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@ -6059,6 +6059,7 @@ class DeepseekModel(TextModel):
@ModelBase.register("DeepseekV2ForCausalLM") @ModelBase.register("DeepseekV2ForCausalLM")
@ModelBase.register("DeepseekV3ForCausalLM") @ModelBase.register("DeepseekV3ForCausalLM")
@ModelBase.register("KimiVLForConditionalGeneration")
class DeepseekV2Model(TextModel): class DeepseekV2Model(TextModel):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2 model_arch = gguf.MODEL_ARCH.DEEPSEEK2
@ -6161,6 +6162,13 @@ class DeepseekV2Model(TextModel):
_experts: list[dict[str, Tensor]] | None = None _experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# skip vision tensors and remove "language_model." for Kimi-VL
if "vision_tower" in name or "multi_modal_projector" in name:
return []
if name.startswith("language_model."):
name = name.replace("language_model.", "")
# rename e_score_correction_bias tensors # rename e_score_correction_bias tensors
if name.endswith("e_score_correction_bias"): if name.endswith("e_score_correction_bias"):
name = name.replace("e_score_correction_bias", "e_score_correction.bias") name = name.replace("e_score_correction_bias", "e_score_correction.bias")

6
gguf-py/gguf/vocab.py
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@ -312,7 +312,11 @@ class SpecialVocab:
with open(config_file, encoding = 'utf-8') as f: with open(config_file, encoding = 'utf-8') as f:
config = json.load(f) config = json.load(f)
for typ in self.special_token_types: for typ in self.special_token_types:
self._set_special_token(typ, config.get(f'{typ}_token_id')) token_id = config.get(f'{typ}_token_id')
# If not found at root, check in text_config (for multimodal models like Kimi-VL)
if token_id is None and 'text_config' in config:
token_id = config['text_config'].get(f'{typ}_token_id')
self._set_special_token(typ, token_id)
return True return True