Method from_pretrained

llama_cpp/llama.py:2316–2447

Create a Llama model from a pretrained model name or path. This method requires the huggingface-hub package. You can install it with `pip install huggingface-hub`. Args: repo_id: The model repo id. filename: A filename or glob pattern to match the mod

(
        cls,
        repo_id: str,
        filename: Optional[str],
        additional_files: Optional[List] = None,
        local_dir: Optional[Union[str, os.PathLike[str]]] = None,
        local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto",
        cache_dir: Optional[Union[str, os.PathLike[str]]] = None,
        **kwargs: Any,
    )

    @classmethod
    def from_pretrained(
        cls,
        repo_id: str,
        filename: Optional[str],
        additional_files: Optional[List] = None,
        local_dir: Optional[Union[str, os.PathLike[str]]] = None,
        local_dir_use_symlinks: Union[bool, Literal["auto"]] = "auto",
        cache_dir: Optional[Union[str, os.PathLike[str]]] = None,
        **kwargs: Any,
    ) -> "Llama":
        """Create a Llama model from a pretrained model name or path.
        This method requires the huggingface-hub package.
        You can install it with `pip install huggingface-hub`.

        Args:
            repo_id: The model repo id.
            filename: A filename or glob pattern to match the model file in the repo.
            additional_files: A list of filenames or glob patterns to match additional model files in the repo.
            local_dir: The local directory to save the model to.
            local_dir_use_symlinks: Whether to use symlinks when downloading the model.
            **kwargs: Additional keyword arguments to pass to the Llama constructor.

        Returns:
            A Llama model."""
        try:
            from huggingface_hub import hf_hub_download, HfFileSystem
            from huggingface_hub.utils import validate_repo_id
        except ImportError:
            raise ImportError(
                "Llama.from_pretrained requires the huggingface-hub package. "
                "You can install it with `pip install huggingface-hub`."
            )

        validate_repo_id(repo_id)

        hffs = HfFileSystem()

        files = [
            file["name"] if isinstance(file, dict) else file
            for file in hffs.ls(repo_id, recursive=True)
        ]

        # split each file into repo_id, subfolder, filename
        file_list: List[str] = []
        for file in files:
            rel_path = Path(file).relative_to(repo_id)
            file_list.append(str(rel_path))

        # find the only/first shard file:
        matching_files = [file for file in file_list if fnmatch.fnmatch(file, filename)]  # type: ignore

        if len(matching_files) == 0:
            raise ValueError(
                f"No file found in {repo_id} that match {filename}\n\n"
                f"Available Files:\n{json.dumps(file_list)}"
            )

        if len(matching_files) > 1: