remove deprecated registration api
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jeromeku
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1 changed files with 0 additions and 309 deletions
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@ -1,309 +0,0 @@
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from functools import partial
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from typing import Callable, Literal
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from unsloth.registry._llama import LlamaMeta3_1, LlamaMeta3_2
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from unsloth.registry.common import ModelInfo, ModelMeta
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# _IS_LLAMA_REGISTERED = False
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# _IS_LLAMA_VISION_REGISTERED = False
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# _IS_QWEN_REGISTERED = False
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# _IS_QWEN_VL_REGISTERED = False
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_IS_GEMMA_REGISTERED = False
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_IS_PHI_REGISTERED = False
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_IS_PHI_INSTRUCT_REGISTERED = False
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# class PhiModelInfo(ModelInfo):
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# @classmethod
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# def construct_model_name(
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# cls, base_name, version, size, quant_type, instruct_tag
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# ):
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# key = f"{base_name}-{version}"
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# key = cls.append_instruct_tag(key, instruct_tag)
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# key = cls.append_quant_type(key, quant_type)
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# return key
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# # Qwen text only models
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# # NOTE: Qwen vision models will be registered separately
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# _PHI_INFO = {
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# "org": "microsoft",
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# "base_name": "phi",
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# "model_versions": ["4"],
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# "model_sizes": {"4": [None]}, # -1 means only 1 size
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# "instruct_tags": [None],
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# "is_multimodal": False,
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# "model_info_cls": PhiModelInfo,
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# }
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# _PHI_INSTRUCT_INFO = {
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# "org": "microsoft",
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# "base_name": "Phi",
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# "model_versions": ["4"],
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# "model_sizes": {"4": [None]}, # -1 means only 1 size
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# "instruct_tags": ["mini-instruct"],
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# "is_multimodal": False,
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# "model_info_cls": PhiModelInfo,
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# }
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MODEL_REGISTRY: dict[str, ModelInfo] = {}
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def register_model(
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model_info_cls: ModelInfo,
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org: str,
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base_name: str,
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version: str,
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size: int,
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instruct_tag: str = None,
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quant_type: Literal["bnb", "unsloth"] = None,
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is_multimodal: bool = False,
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name: str = None,
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):
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name = name or model_info_cls.construct_model_name(
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base_name=base_name,
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version=version,
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size=size,
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quant_type=quant_type,
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instruct_tag=instruct_tag,
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)
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key = f"{org}/{name}"
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if key in MODEL_REGISTRY:
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raise ValueError(f"Model {key} already registered")
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MODEL_REGISTRY[key] = model_info_cls(
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org=org,
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base_name=base_name,
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version=version,
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size=size,
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is_multimodal=is_multimodal,
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instruct_tag=instruct_tag,
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quant_type=quant_type,
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name=name,
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)
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# def _register_models(model_info: dict):
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# org = model_info["org"]
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# base_name = model_info["base_name"]
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# instruct_tags = model_info["instruct_tags"]
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# model_versions = model_info["model_versions"]
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# model_sizes = model_info["model_sizes"]
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# is_multimodal = model_info["is_multimodal"]
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# model_info_cls = model_info["model_info_cls"]
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# for version in model_versions:
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# for size in model_sizes[version]:
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# for instruct_tag in instruct_tags:
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# for quant_type in QUANT_TYPES:
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# _org = "unsloth" if quant_type is not None else org
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# register_model(
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# model_info_cls=model_info_cls,
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# org=_org,
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# base_name=base_name,
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# version=version,
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# size=size,
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# instruct_tag=instruct_tag,
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# quant_type=quant_type,
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# is_multimodal=is_multimodal,
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# )
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def _register_models(model_meta: ModelMeta):
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org = model_meta.org
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base_name = model_meta.base_name
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instruct_tags = model_meta.instruct_tags
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model_version = model_meta.model_version
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model_sizes = model_meta.model_sizes
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is_multimodal = model_meta.is_multimodal
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quant_types = model_meta.quant_types
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model_info_cls = model_meta.model_info_cls
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for size in model_sizes:
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for instruct_tag in instruct_tags:
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for quant_type in quant_types:
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_org = "unsloth" if quant_type is not None else org
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register_model(
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model_info_cls=model_info_cls,
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org=_org,
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base_name=base_name,
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version=model_version,
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size=size,
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instruct_tag=instruct_tag,
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quant_type=quant_type,
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is_multimodal=is_multimodal,
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)
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def register_llama_models():
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global _IS_LLAMA_REGISTERED
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if _IS_LLAMA_REGISTERED:
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return
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_register_models(LlamaMeta3_1)
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_register_models(LlamaMeta3_2)
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_IS_LLAMA_REGISTERED = True
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def register_llama_vision_models():
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global _IS_LLAMA_VISION_REGISTERED
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if _IS_LLAMA_VISION_REGISTERED:
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return
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_register_models(_LLAMA_VISION_INFO)
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_IS_LLAMA_VISION_REGISTERED = True
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def register_qwen_models():
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global _IS_QWEN_REGISTERED
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if _IS_QWEN_REGISTERED:
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return
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_register_models(_QWEN_INFO)
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_IS_QWEN_REGISTERED = True
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def register_qwen_vl_models():
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global _IS_QWEN_VL_REGISTERED
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if _IS_QWEN_VL_REGISTERED:
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return
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_register_models(_QWEN_VL_INFO)
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_IS_QWEN_VL_REGISTERED = True
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def register_gemma_models():
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global _IS_GEMMA_REGISTERED
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_register_models(_GEMMA_INFO)
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_IS_GEMMA_REGISTERED = True
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def register_phi_models():
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global _IS_PHI_REGISTERED
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if _IS_PHI_REGISTERED:
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return
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_register_models(_PHI_INFO)
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_IS_PHI_REGISTERED = True
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def register_phi_instruct_models():
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global _IS_PHI_INSTRUCT_REGISTERED
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if _IS_PHI_INSTRUCT_REGISTERED:
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return
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_register_models(_PHI_INSTRUCT_INFO)
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_IS_PHI_INSTRUCT_REGISTERED = True
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def _base_name_filter(model_info: ModelInfo, base_name: str):
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return model_info.base_name == base_name
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def _get_models(filter_func: Callable[[ModelInfo], bool] = _base_name_filter):
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return {k: v for k, v in MODEL_REGISTRY.items() if filter_func(v)}
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def get_llama_models(version: str = None):
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if not _IS_LLAMA_REGISTERED:
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register_llama_models()
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llama_models: dict[str, ModelInfo] = _get_models(
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partial(_base_name_filter, base_name=LlamaMeta3_1.base_name)
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)
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if version is not None:
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llama_models = {
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k: v for k, v in llama_models.items() if v.version == version
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}
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return llama_models
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def get_llama_vision_models():
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if not _IS_LLAMA_VISION_REGISTERED:
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register_llama_vision_models()
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return _get_models(
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lambda model_info: model_info.base_name
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== _LLAMA_VISION_INFO["base_name"]
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and model_info.is_multimodal
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)
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def get_qwen_models():
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if not _IS_QWEN_REGISTERED:
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register_qwen_models()
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return _get_models(
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lambda model_info: model_info.base_name == _QWEN_INFO["base_name"]
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)
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def get_qwen_vl_models():
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if not _IS_QWEN_VL_REGISTERED:
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register_qwen_vl_models()
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return _get_models(
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lambda model_info: model_info.base_name == _QWEN_VL_INFO["base_name"]
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)
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def get_gemma_models():
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if not _IS_GEMMA_REGISTERED:
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register_gemma_models()
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return _get_models(
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lambda model_info: model_info.base_name == _GEMMA_INFO["base_name"]
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)
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def get_phi_models():
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if not _IS_PHI_REGISTERED:
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register_phi_models()
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return _get_models(
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lambda model_info: model_info.base_name == _PHI_INFO["base_name"]
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)
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def get_phi_instruct_models():
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if not _IS_PHI_INSTRUCT_REGISTERED:
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register_phi_instruct_models()
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return _get_models(
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lambda model_info: model_info.base_name
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== _PHI_INSTRUCT_INFO["base_name"]
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)
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if __name__ == "__main__":
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from huggingface_hub import HfApi
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api = HfApi()
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def get_model_info(
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model_id: str, properties: list[str] = None
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) -> ModelInfo:
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try:
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model_info: ModelInfo = api.model_info(model_id, expand=properties)
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except Exception as e:
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print(f"Error getting model info for {model_id}: {e}")
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model_info = None
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return model_info
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register_llama_models()
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llama3_1_models = get_llama_models(version="3.2")
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missing_models = []
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for k, v in llama3_1_models.items():
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model_info = get_model_info(v.model_path)
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if model_info is None:
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# print unicode cross mark followed by model k
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print(f"\u2718 {k}")
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missing_models.append(k)
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if len(missing_models) == 0:
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# print unicode checkmark
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print("\u2713 All models found!")
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