from_model_id() — langchain Function Reference

Architecture documentation for the from_model_id() function in huggingface_pipeline.py from the langchain codebase.

Function python LangChainCore LanguageModelBase

Entity Profile

LangChainCore→ LanguageModelBase→ from_model_id() — langchain Function Reference

Dependency Diagram

graph TD
  834807da_062f_4f2f_6975_506464144209["from_model_id()"]
  54333c82_6644_5574_2c41_4cc818ce3595["HuggingFacePipeline"]
  834807da_062f_4f2f_6975_506464144209 -->|defined in| 54333c82_6644_5574_2c41_4cc818ce3595
  style 834807da_062f_4f2f_6975_506464144209 fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py lines 106–301

    def from_model_id(
        cls,
        model_id: str,
        task: str,
        backend: str = "default",
        device: int | None = None,
        device_map: str | None = None,
        model_kwargs: dict | None = None,
        pipeline_kwargs: dict | None = None,
        batch_size: int = DEFAULT_BATCH_SIZE,
        **kwargs: Any,
    ) -> HuggingFacePipeline:
        """Construct the pipeline object from model_id and task."""
        try:
            from transformers import (  # type: ignore[import]
                AutoModelForCausalLM,
                AutoModelForSeq2SeqLM,
                AutoTokenizer,
            )
            from transformers import pipeline as hf_pipeline  # type: ignore[import]

        except ImportError as e:
            msg = (
                "Could not import transformers python package. "
                "Please install it with `pip install transformers`."
            )
            raise ValueError(msg) from e

        _model_kwargs = model_kwargs.copy() if model_kwargs else {}
        if device_map is not None:
            if device is not None:
                msg = (
                    "Both `device` and `device_map` are specified. "
                    "`device` will override `device_map`. "
                    "You will most likely encounter unexpected behavior."
                    "Please remove `device` and keep "
                    "`device_map`."
                )
                raise ValueError(msg)

            if "device_map" in _model_kwargs:
                msg = "`device_map` is already specified in `model_kwargs`."
                raise ValueError(msg)

            _model_kwargs["device_map"] = device_map
        tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)

        if backend in {"openvino", "ipex"}:
            if task not in VALID_TASKS:
                msg = (
                    f"Got invalid task {task}, "
                    f"currently only {VALID_TASKS} are supported"
                )
                raise ValueError(msg)

            err_msg = f"Backend: {backend} {IMPORT_ERROR.format(f'optimum[{backend}]')}"
            if not is_optimum_intel_available():
                raise ImportError(err_msg)

            # TODO: upgrade _MIN_OPTIMUM_VERSION to 1.22 after release
            min_optimum_version = (
                "1.22"
                if backend == "ipex" and task != "text-generation"
                else _MIN_OPTIMUM_VERSION
            )
            if is_optimum_intel_version("<", min_optimum_version):
                msg = (
                    f"Backend: {backend} requires optimum-intel>="
                    f"{min_optimum_version}. You can install it with pip: "
                    "`pip install --upgrade --upgrade-strategy eager "
                    f"`optimum[{backend}]`."
                )
                raise ImportError(msg)

            if backend == "openvino":
                if not is_openvino_available():
                    raise ImportError(err_msg)

                from optimum.intel import (  # type: ignore[import]
                    OVModelForCausalLM,
                    OVModelForSeq2SeqLM,