Method from_pretrained

lmdeploy/pytorch/engine/engine.py:215–254

Lmdeploy python inference engine. Args: pretrained_model_name_or_path (str): It could be one of the following options: - i) The model_id of a lmdeploy-quantized model hosted inside a model repo on huggingface.co, such as

(cls,
                        pretrained_model_name_or_path: str,
                        engine_config: PytorchEngineConfig = None,
                        speculative_config: SpeculativeConfig = None,
                        trust_remote_code: bool = False,
                        **kwargs)

    @classmethod
    def from_pretrained(cls,
                        pretrained_model_name_or_path: str,
                        engine_config: PytorchEngineConfig = None,
                        speculative_config: SpeculativeConfig = None,
                        trust_remote_code: bool = False,
                        **kwargs):
        """Lmdeploy python inference engine.

        Args:
            pretrained_model_name_or_path (str):
                It could be one of the following options:
                    - i) The model_id of a lmdeploy-quantized model hosted
                      inside a model repo on huggingface.co, such as
                      "InternLM/internlm-chat-20b-4bit",
                      "lmdeploy/llama2-chat-70b-4bit", etc.
                    - ii) The model_id of a model hosted inside a model repo
                      on huggingface.co, such as "InternLM/internlm-chat-7b",
                      "Qwen/Qwen-7B-Chat ", "baichuan-inc/Baichuan2-7B-Chat"
                      and so on.
            engine_config (PytorchEngineConfig): Pytorch engine config.
            trust_remote_code (bool): Trust remote code
        """
        if engine_config is not None and engine_config.enable_mp_engine:
            from .mp_engine import build_mp_engine
            backend = engine_config.mp_engine_backend
            return build_mp_engine(
                backend=backend,
                model_path=pretrained_model_name_or_path,
                engine_config=engine_config,
                speculative_config=speculative_config,
                trust_remote_code=trust_remote_code
            )
        if len(kwargs) > 0:
            logger.debug(f'Get unexpected kwargs: {kwargs}')
        return cls(
            model_path=pretrained_model_name_or_path,
            engine_config=engine_config,
            speculative_config=speculative_config,
            trust_remote_code=trust_remote_code
        )

    def _download_adapters(self, adapters: dict[str, str], engine_config: PytorchEngineConfig):
        """Download adapters."""