Move dequant_model to after the text_config merge
Add new kimi-k2.5 keys to mtmd convert Update V_MMPROJ tensor mapping for new mm_projector.proj keys Update V_M_IMP_NORM for new mm_projector.pre_norm key
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Aes Sedai
parent
41ea26144e
commit
042c3cb8c5
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@ -160,8 +160,6 @@ class ModelBase:
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self.ftype = gguf.LlamaFileType.MOSTLY_F16
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logger.info("heuristics unable to detect tensor dtype, defaulting to --outtype f16")
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self.dequant_model()
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# Configure GGUF Writer
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self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file,
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split_max_tensors=split_max_tensors, split_max_size=split_max_size, dry_run=dry_run, small_first_shard=small_first_shard)
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@ -527,6 +525,8 @@ class ModelBase:
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return ()
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def prepare_tensors(self):
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self.dequant_model()
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# Handle empty tensor_map for models with block_count=0 (like MobileNetV5)
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if self.tensor_map.mapping:
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max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,")
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@ -1808,7 +1808,7 @@ class MmprojModel(ModelBase):
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preprocessor_config: dict[str, Any]
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global_config: dict[str, Any]
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n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers"]
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n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers", "vt_num_hidden_layers"]
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has_vision_encoder: bool = True # by default
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has_audio_encoder: bool = False
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@ -1863,7 +1863,15 @@ class MmprojModel(ModelBase):
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preprocessor_config_path = self.dir_model / "preprocessor_config.json"
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if preprocessor_config_path.is_file():
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with open(preprocessor_config_path, "r", encoding="utf-8") as f:
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self.preprocessor_config = json.load(f)
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cfg = json.load(f)
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# move media_proc_cfg to root level for compat
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if "media_proc_cfg" in cfg:
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cfg = {
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**cfg,
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**cfg["media_proc_cfg"],
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}
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# merge configs
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self.preprocessor_config = {**self.preprocessor_config, **cfg}
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# prefer processor_config.json if possible
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processor_config_path = self.dir_model / "processor_config.json"
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@ -1912,10 +1920,10 @@ class MmprojModel(ModelBase):
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self.image_size = self.find_vparam(["image_size"])
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self.gguf_writer.add_vision_image_size(self.image_size)
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self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
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self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size"]))
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self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size"]))
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self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "vt_hidden_size"]))
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self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"]))
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self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
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self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads"]))
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self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "vt_num_attention_heads"]))
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# preprocessor config
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image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
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@ -7360,6 +7368,7 @@ class DeepseekModel(TextModel):
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"DeepseekV2ForCausalLM",
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"DeepseekV3ForCausalLM",
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"KimiVLForConditionalGeneration",
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"KimiK25ForConditionalGeneration",
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"YoutuForCausalLM",
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"YoutuVLForConditionalGeneration",
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)
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@ -7478,8 +7487,8 @@ class DeepseekV2Model(TextModel):
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_experts: list[dict[str, Tensor]] | None = None
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# skip vision tensors and remove "language_model." for Kimi-VL
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if "vision_tower" in name or "multi_modal_projector" in name:
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# skip vision tensors and remove "language_model." for Kimi-VL and Kimi-K2.5
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if "vision_tower" in name or "multi_modal_projector" in name or "mm_projector" in name:
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return
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if name.startswith("siglip2.") or name.startswith("merger."):
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return
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@ -10614,7 +10623,7 @@ class MistralMoeModel(DeepseekV2Model):
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self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
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if name.startswith("vision_") or name.startswith("patch_merger.") or "mm_projector" in name:
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if name.startswith("vision_") or name.startswith("patch_merger."):
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return
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# rename certain tensors so that we can reuse DeepseekV2Model modify_tensors logic
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@ -10679,7 +10688,7 @@ class LightOnOCRVisionModel(LlavaVisionModel):
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yield from super().modify_tensors(data_torch, name, bid)
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@ModelBase.register("KimiVLForConditionalGeneration")
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@ModelBase.register("KimiVLForConditionalGeneration", "KimiK25ForConditionalGeneration")
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class KimiVLModel(MmprojModel):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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@ -10696,9 +10705,17 @@ class KimiVLModel(MmprojModel):
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self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-5))
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
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is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name or "mm_projector" in name
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if is_vision_tensor:
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# update names:
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# "mm_projector.proj.0" -> "mm_projector.proj.linear_1.",
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# "mm_projector.proj.2" -> "mm_projector.proj.linear_2.",
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if "proj.0." in name:
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name = name.replace(".0.", ".linear_1.")
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if "proj.2." in name:
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name = name.replace(".2.", ".linear_2.")
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if "pos_emb.weight" in name:
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data_torch = data_torch.view(data_torch.shape[0] * data_torch.shape[1], data_torch.shape[2])
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@ -1255,6 +1255,7 @@ class TensorNameMap:
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MODEL_TENSOR.V_MMPROJ: (
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"multi_modal_projector.linear_{bid}",
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"mm_projector.proj.linear_{bid}",
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"visual.merger.mlp.{bid}", # qwen2vl
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"merger.mlp.{bid}",
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),
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@ -1490,6 +1491,7 @@ class TensorNameMap:
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"multi_modal_projector.norm",
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"multi_modal_projector.layer_norm",
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"multi_modal_projector.pre_norm",
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"mm_projector.pre_norm", # Kimi-K2.5
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"pre_mm_projector_norm",
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"model.vision.linear_proj.norm1", # cogvlm
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"merger.ln_q",
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