add deepseekv3 and llama4
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@@ -13,7 +13,7 @@ from transformers import GenerationConfig, PretrainedConfig
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from transformers.models.auto.image_processing_auto import (
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from transformers.models.auto.image_processing_auto import (
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get_image_processor_config)
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get_image_processor_config)
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from transformers.models.auto.modeling_auto import (
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from transformers.models.auto.modeling_auto import (
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MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
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MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_MAPPING_NAMES)
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from transformers.utils import CONFIG_NAME as HF_CONFIG_NAME
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from transformers.utils import CONFIG_NAME as HF_CONFIG_NAME
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from vllm.envs import VLLM_USE_MODELSCOPE
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from vllm.envs import VLLM_USE_MODELSCOPE
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@@ -229,13 +229,16 @@ def get_config(
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model_type = MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[config.model_type]
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model_type = MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[config.model_type]
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config.update({"architectures": [model_type]})
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config.update({"architectures": [model_type]})
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# Some composite config classes (e.g. Llama4Config) may not preserve
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# Architecture mapping for models without explicit architectures field
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# the 'architectures' field from config.json. Restore it from the
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if not getattr(config, "architectures", None):
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# raw config_dict if needed.
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if config.model_type not in MODEL_MAPPING_NAMES:
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if config_format == ConfigFormat.HF:
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logger.warning(
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raw_archs = config_dict.get("architectures")
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"Model config does not have a top-level 'architectures' "
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if raw_archs and not getattr(config, "architectures", None):
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"field: expecting `hf_overrides={'architectures': "
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config.architectures = raw_archs
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"['...']}` to be passed in engine args.")
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else:
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model_type = MODEL_MAPPING_NAMES[config.model_type]
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config.update({"architectures": [model_type]})
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patch_rope_scaling(config)
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patch_rope_scaling(config)
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