src/transformers/modeling_utils.py/PreTrainedModel/from_pretrained
class PreTrainedModel:    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
        r"""
        Instantiate a pretrained pytorch model from a pre-trained model configuration.

        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
        the model, you should first set it back in training mode with `model.train()`.

        The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
        task.

        The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
        weights are discarded.

        Parameters:
            pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
                Can be either:

                    - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                      Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced under a
                      user or organization name, like `dbmdz/bert-base-german-cased`.
                    - A path to a *directory* containing model weights saved using
                      [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
                    - A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
                      this case, `from_tf` should be set to `True` and a configuration object should be provided as
                      `config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
                      PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
                    - A path or url to a model folder containing a *flax checkpoint file* in *.msgpack* format (e.g,
                      `./flax_model/` containing `flax_model.msgpack`). In this case, `from_flax` should be set to
                      `True`.
                    - `None` if you are both providing the configuration and state dictionary (resp. with keyword
                      arguments `config` and `state_dict`).
            model_args (sequence of positional arguments, *optional*):
                All remaining positional arguments will be passed to the underlying model's `__init__` method.
            config (`Union[PretrainedConfig, str, os.PathLike]`, *optional*):
                Can be either:

                    - an instance of a class derived from [`PretrainedConfig`],
                    - a string or path valid as input to [`~PretrainedConfig.from_pretrained`].

                Configuration for the model to use instead of an automatically loaded configuration. Configuration can
                be automatically loaded when:

                    - The model is a model provided by the library (loaded with the *model id* string of a pretrained
                      model).
                    - The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
                      save directory.
                    - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
                      configuration JSON file named *config.json* is found in the directory.
            state_dict (`Dict[str, torch.Tensor]`, *optional*):
                A state dictionary to use instead of a state dictionary loaded from saved weights file.

                This option can be used if you want to create a model from a pretrained configuration but load your own
                weights. In this case though, you should check if using [`~PreTrainedModel.save_pretrained`] and
                [`~PreTrainedModel.from_pretrained`] is not a simpler option.
            cache_dir (`Union[str, os.PathLike]`, *optional*):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            from_tf (`bool`, *optional*, defaults to `False`):
                Load the model weights from a TensorFlow checkpoint save file (see docstring of
                `pretrained_model_name_or_path` argument).
            from_flax (`bool`, *optional*, defaults to `False`):
                Load the model weights from a Flax checkpoint save file (see docstring of
                `pretrained_model_name_or_path` argument).
            ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
                Whether or not to raise an error if some of the weights from the checkpoint do not have the same size
                as the weights of the model (if for instance, you are instantiating a model with 10 labels from a
                checkpoint with 3 labels).
            force_download (`bool`, *optional*, defaults to `False`):
                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
                cached versions if they exist.
            resume_download (`bool`, *optional*, defaults to `False`):
                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                file exists.
            proxies (`Dict[str, str]`, *optional*):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            output_loading_info(`bool`, *optional*, defaults to `False`):
                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
            local_files_only(`bool`, *optional*, defaults to `False`):
                Whether or not to only look at local files (i.e., do not try to download the model).
            use_auth_token (`str` or `bool`, *optional*):
                The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, will use
                the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
            revision (`str`, *optional*, defaults to `"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
                identifier allowed by git.

                <Tip>

                To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>".

                </Tip>

            mirror (`str`, *optional*):
                Mirror source to accelerate downloads in China. If you are from China and have an accessibility
                problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
                Please refer to the mirror site for more information.
            _fast_init(`bool`, *optional*, defaults to `True`):
                Whether or not to disable fast initialization.

                <Tip warning={true}>

                One should only disable *_fast_init* to ensure backwards compatibility with `transformers.__version__ <
                4.6.0` for seeded model initialization. This argument will be removed at the next major version. See
                [pull request 11471](https://github.com/huggingface/transformers/pull/11471) for more information.

                </Tip>

            > Parameters for big model inference

            low_cpu_mem_usage(`bool`, *optional*):
                Tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model.
                This is an experimental feature and a subject to change at any moment.
            torch_dtype (`str` or `torch.dtype`, *optional*):
                Override the default `torch.dtype` and load the model under a specific `dtype`. The different options
                are:

                1. `torch.float16` or `torch.bfloat16` or `torch.float`: load in a specified
                  `dtype`, ignoring the model's `config.torch_dtype` if one exists. If not specified
                  - the model will get loaded in `torch.float` (fp32).

                2. `"auto"` - A `torch_dtype` entry in the `config.json` file of the model will be
                  attempted to be used. If this entry isn't found then next check the `dtype` of the first weight in
                  the checkpoint that's of a floating point type and use that as `dtype`. This will load the model
                  using the `dtype` it was saved in at the end of the training. It can't be used as an indicator of how
                  the model was trained. Since it could be trained in one of half precision dtypes, but saved in fp32.

                <Tip>

                For some models the `dtype` they were trained in is unknown - you may try to check the model's paper or
                reach out to the authors and ask them to add this information to the model's card and to insert the
                `torch_dtype` entry in `config.json` on the hub.

                </Tip>

            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
                A map that specifies where each submodule should go. It doesn't need to be refined to each
                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
                same device.

                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
                more information about each option see [designing a device
                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
            max_memory (`Dict`, *optional*):
                A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
                GPU and the available CPU RAM if unset.
            offload_folder (`str` or `os.PathLike`, *optional*):
                If the `device_map` contains any value `"disk"`, the folder where we will offload weights.
            offload_state_dict (`bool`, *optional*):
                If `True`, will temporarily offload the CPU state dict to the hard drive to avoid getting out of CPU
                RAM if the weight of the CPU state dict + the biggest shard of the checkpoint does not fit. Defaults to
                `True` when there is some disk offload.
            load_in_8bit (`bool`, *optional*, defaults to `False`):
                If `True`, will convert the loaded model into mixed-8bit quantized model. To use this feature please
                install `bitsandbytes` compiled with your CUDA version by running `pip install -i
                https://test.pypi.org/simple/ bitsandbytes-cudaXXX` where XXX is your CUDA version (e.g. 11.6 = 116).
                Make also sure that you have enough GPU RAM to store half of the model size since the 8bit modules are
                not compiled and adapted for CPUs.
            quantization_config (`Dict`, *optional*):
                A dictionary of configuration parameters for the `bitsandbytes` library and loading the model using
                advanced features such as offloading in fp32 on CPU or on disk.
            subfolder (`str`, *optional*, defaults to `""`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
                specify the folder name here.
            variant (`str`, *optional*):
                If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is
                ignored when using `from_tf` or `from_flax`.

            kwargs (remaining dictionary of keyword arguments, *optional*):
                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
                `output_attentions=True`). Behaves differently depending on whether a `config` is provided or
                automatically loaded:

                    - If a configuration is provided with `config`, `**kwargs` will be directly passed to the
                      underlying model's `__init__` method (we assume all relevant updates to the configuration have
                      already been done)
                    - If a configuration is not provided, `kwargs` will be first passed to the configuration class
                      initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
                      corresponds to a configuration attribute will be used to override said attribute with the
                      supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
                      will be passed to the underlying model's `__init__` function.

        <Tip>

        Activate the special ["offline-mode"](https://huggingface.co/transformers/installation.html#offline-mode) to
        use this method in a firewalled environment.

        </Tip>

        Examples:

        ```python
        >>> from transformers import BertConfig, BertModel

        >>> # Download model and configuration from huggingface.co and cache.
        >>> model = BertModel.from_pretrained("bert-base-uncased")
        >>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
        >>> model = BertModel.from_pretrained("./test/saved_model/")
        >>> # Update configuration during loading.
        >>> model = BertModel.from_pretrained("bert-base-uncased", output_attentions=True)
        >>> assert model.config.output_attentions == True
        >>> # Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).
        >>> config = BertConfig.from_json_file("./tf_model/my_tf_model_config.json")
        >>> model = BertModel.from_pretrained("./tf_model/my_tf_checkpoint.ckpt.index", from_tf=True, config=config)
        >>> # Loading from a Flax checkpoint file instead of a PyTorch model (slower)
        >>> model = BertModel.from_pretrained("bert-base-uncased", from_flax=True)
        ```

        * `low_cpu_mem_usage` algorithm:

        This is an experimental function that loads the model using ~1x model size CPU memory

        Here is how it works:

        1. save which state_dict keys we have
        2. drop state_dict before the model is created, since the latter takes 1x model size CPU memory
        3. after the model has been instantiated switch to the meta device all params/buffers that
        are going to be replaced from the loaded state_dict
        4. load state_dict 2nd time
        5. replace the params/buffers from the state_dict

        Currently, it can't handle deepspeed ZeRO stage 3 and ignores loading errors

        """
        config = kwargs.pop("config", None)
        state_dict = kwargs.pop("state_dict", None)
        cache_dir = kwargs.pop("cache_dir", None)
        from_tf = kwargs.pop("from_tf", False)
        from_flax = kwargs.pop("from_flax", False)
        ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", False)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        output_loading_info = kwargs.pop("output_loading_info", False)
        local_files_only = kwargs.pop("local_files_only", False)
        use_auth_token = kwargs.pop("use_auth_token", None)
        revision = kwargs.pop("revision", None)
        trust_remote_code = kwargs.pop("trust_remote_code", None)
        _ = kwargs.pop("mirror", None)
        from_pipeline = kwargs.pop("_from_pipeline", None)
        from_auto_class = kwargs.pop("_from_auto", False)
        _fast_init = kwargs.pop("_fast_init", True)
        torch_dtype = kwargs.pop("torch_dtype", None)
        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", None)
        device_map = kwargs.pop("device_map", None)
        max_memory = kwargs.pop("max_memory", None)
        offload_folder = kwargs.pop("offload_folder", None)
        offload_state_dict = kwargs.pop("offload_state_dict", False)
        load_in_8bit = kwargs.pop("load_in_8bit", False)
        quantization_config = kwargs.pop("quantization_config", None)
        subfolder = kwargs.pop("subfolder", "")
        commit_hash = kwargs.pop("_commit_hash", None)
        variant = kwargs.pop("variant", None)
        use_safetensors = kwargs.pop("use_safetensors", None if is_safetensors_available() else False)

        if trust_remote_code is True:
            logger.warning(
                "The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is"
                " ignored."
            )
        if device_map is not None:
            if low_cpu_mem_usage is None:
                low_cpu_mem_usage = True
            elif not low_cpu_mem_usage:
                raise ValueError("Passing along a `device_map` requires `low_cpu_mem_usage=True`")

        if low_cpu_mem_usage:
            # low_cpu_mem_usage requires PyTorch >= 1.9 to have the meta device.
            require_version_core("torch>=1.9")
            if device_map is not None:
                # The max memory utils require PyTorch >= 1.10 to have torch.cuda.mem_get_info.
                require_version_core("torch>=1.10")

            if is_deepspeed_zero3_enabled():
                raise ValueError(
                    "DeepSpeed Zero-3 is not compatible with `low_cpu_mem_usage=True` or with passing a `device_map`."
                )
            elif not is_accelerate_available():
                raise ImportError(
                    "Using `low_cpu_mem_usage=True` or a `device_map` requires Accelerate: `pip install accelerate`"
                )

        if quantization_config is None:
            quantization_config, kwargs = BitsAndBytesConfig.from_dict(
                config_dict={"load_in_8bit": load_in_8bit}, return_unused_kwargs=True, **kwargs
            )
        elif quantization_config is not None:
            load_in_8bit = quantization_config.load_in_8bit

            quantization_config_kwargs = {
                k: v for k, v in kwargs.items() if k in inspect.signature(BitsAndBytesConfig).parameters
            }

            if len(quantization_config_kwargs) > 0:
                raise ValueError(
                    "You can't pass `load_in_8bit` or any other `BitsAndBytesConfig` argument as a kwarg when passing "
                    "`quantization_config` argument at the same time."
                )

        if load_in_8bit:
            if not (is_accelerate_available() and is_bitsandbytes_available()):
                raise ImportError(
                    "Using `load_in_8bit=True` requires Accelerate: `pip install accelerate` and the latest version of"
                    " bitsandbytes `pip install -i https://test.pypi.org/simple/ bitsandbytes` or"
                    " pip install bitsandbytes` "
                )
            if torch_dtype != torch.float16:
                # We force the `dtype` to be float16, this is a requirement from `bitsandbytes`
                logger.warning(
                    f"Overriding torch_dtype={torch_dtype} with `torch_dtype=torch.float16` due to "
                    "requirements of `bitsandbytes` to enable model loading in mixed int8. "
                    "Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning."
                )
                torch_dtype = torch.float16

            if device_map is None:
                raise ValueError(
                    "A device map needs to be passed to run convert models into mixed-int8 format. Please run"
                    "`.from_pretrained` with `device_map='auto'`"
                )
            if from_tf or from_flax:
                raise ValueError(
                    "Converting into mixed 8-bit weights from tf/flax weights is currently not supported, please make"
                    " sure the weights are in PyTorch format."
                )

        from_pt = not (from_tf | from_flax)

        user_agent = {"file_type": "model", "framework": "pytorch", "from_auto_class": from_auto_class}
        if from_pipeline is not None:
            user_agent["using_pipeline"] = from_pipeline

        if is_offline_mode() and not local_files_only:
            logger.info("Offline mode: forcing local_files_only=True")
            local_files_only = True

        # Load config if we don't provide a configuration
        if not isinstance(config, PretrainedConfig):
            config_path = config if config is not None else pretrained_model_name_or_path
            config, model_kwargs = cls.config_class.from_pretrained(
                config_path,
                cache_dir=cache_dir,
                return_unused_kwargs=True,
                force_download=force_download,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                revision=revision,
                subfolder=subfolder,
                _from_auto=from_auto_class,
                _from_pipeline=from_pipeline,
                **kwargs,
            )
        else:
            model_kwargs = kwargs

        if commit_hash is None:
            commit_hash = getattr(config, "_commit_hash", None)

        # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
        # index of the files.
        is_sharded = False
        sharded_metadata = None
        # Load model
        loading_info = None

        # Keep in fp32 modules
        keep_in_fp32_modules = None
        use_keep_in_fp32_modules = False

        if pretrained_model_name_or_path is not None:
            pretrained_model_name_or_path = str(pretrained_model_name_or_path)
            is_local = os.path.isdir(pretrained_model_name_or_path)
            if is_local:
                if from_tf and os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
                ):
                    # Load from a TF 1.0 checkpoint in priority if from_tf
                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
                elif from_tf and os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)
                ):
                    # Load from a TF 2.0 checkpoint in priority if from_tf
                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)
                elif from_flax and os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
                ):
                    # Load from a Flax checkpoint in priority if from_flax
                    archive_file = os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)
                elif use_safetensors is not False and os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_NAME, variant))
                ):
                    # Load from a safetensors checkpoint
                    archive_file = os.path.join(
                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_NAME, variant)
                    )
                elif use_safetensors is not False and os.path.isfile(
                    os.path.join(
                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)
                    )
                ):
                    # Load from a sharded safetensors checkpoint
                    archive_file = os.path.join(
                        pretrained_model_name_or_path, subfolder, _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)
                    )
                    is_sharded = True
                elif os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_NAME, variant))
                ):
                    # Load from a PyTorch checkpoint
                    archive_file = os.path.join(
                        pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_NAME, variant)
                    )
                elif os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant))
                ):
                    # Load from a sharded PyTorch checkpoint
                    archive_file = os.path.join(
                        pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant)
                    )
                    is_sharded = True
                # At this stage we don't have a weight file so we will raise an error.
                elif os.path.isfile(
                    os.path.join(pretrained_model_name_or_path, subfolder, TF_WEIGHTS_NAME + ".index")
                ) or os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, TF2_WEIGHTS_NAME)):
                    raise EnvironmentError(
                        f"Error no file named {_add_variant(WEIGHTS_NAME, variant)} found in directory"
                        f" {pretrained_model_name_or_path} but there is a file for TensorFlow weights. Use"
                        " `from_tf=True` to load this model from those weights."
                    )
                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, subfolder, FLAX_WEIGHTS_NAME)):
                    raise EnvironmentError(
                        f"Error no file named {_add_variant(WEIGHTS_NAME, variant)} found in directory"
                        f" {pretrained_model_name_or_path} but there is a file for Flax weights. Use `from_flax=True`"
                        " to load this model from those weights."
                    )
                else:
                    raise EnvironmentError(
                        f"Error no file named {_add_variant(WEIGHTS_NAME, variant)}, {TF2_WEIGHTS_NAME},"
                        f" {TF_WEIGHTS_NAME + '.index'} or {FLAX_WEIGHTS_NAME} found in directory"
                        f" {pretrained_model_name_or_path}."
                    )
            elif os.path.isfile(os.path.join(subfolder, pretrained_model_name_or_path)):
                archive_file = pretrained_model_name_or_path
                is_local = True
            elif os.path.isfile(os.path.join(subfolder, pretrained_model_name_or_path + ".index")):
                if not from_tf:
                    raise ValueError(
                        f"We found a TensorFlow checkpoint at {pretrained_model_name_or_path + '.index'}, please set "
                        "from_tf to True to load from this checkpoint."
                    )
                archive_file = os.path.join(subfolder, pretrained_model_name_or_path + ".index")
                is_local = True
            elif is_remote_url(pretrained_model_name_or_path):
                filename = pretrained_model_name_or_path
                resolved_archive_file = download_url(pretrained_model_name_or_path)
            else:
                # set correct filename
                if from_tf:
                    filename = TF2_WEIGHTS_NAME
                elif from_flax:
                    filename = FLAX_WEIGHTS_NAME
                elif use_safetensors is not False:
                    filename = _add_variant(SAFE_WEIGHTS_NAME, variant)
                else:
                    filename = _add_variant(WEIGHTS_NAME, variant)

                try:
                    # Load from URL or cache if already cached
                    cached_file_kwargs = {
                        "cache_dir": cache_dir,
                        "force_download": force_download,
                        "proxies": proxies,
                        "resume_download": resume_download,
                        "local_files_only": local_files_only,
                        "use_auth_token": use_auth_token,
                        "user_agent": user_agent,
                        "revision": revision,
                        "subfolder": subfolder,
                        "_raise_exceptions_for_missing_entries": False,
                        "_commit_hash": commit_hash,
                    }
                    resolved_archive_file = cached_file(pretrained_model_name_or_path, filename, **cached_file_kwargs)

                    # Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None
                    # result when internet is up, the repo and revision exist, but the file does not.
                    if resolved_archive_file is None and filename == _add_variant(SAFE_WEIGHTS_NAME, variant):
                        # Maybe the checkpoint is sharded, we try to grab the index name in this case.
                        resolved_archive_file = cached_file(
                            pretrained_model_name_or_path,
                            _add_variant(SAFE_WEIGHTS_INDEX_NAME, variant),
                            **cached_file_kwargs,
                        )
                        if resolved_archive_file is not None:
                            is_sharded = True
                        elif use_safetensors:
                            raise EnvironmentError(
                                f" {_add_variant(SAFE_WEIGHTS_NAME, variant)} or {_add_variant(SAFE_WEIGHTS_INDEX_NAME, variant)} and thus cannot be loaded with `safetensors`. Please make sure that the model has been saved with `safe_serialization=True` or do not set `use_safetensors=True`."
                            )
                        else:
                            # This repo has no safetensors file of any kind, we switch to PyTorch.
                            filename = _add_variant(WEIGHTS_NAME, variant)
                            resolved_archive_file = cached_file(
                                pretrained_model_name_or_path, filename, **cached_file_kwargs
                            )
                    if resolved_archive_file is None and filename == _add_variant(WEIGHTS_NAME, variant):
                        # Maybe the checkpoint is sharded, we try to grab the index name in this case.
                        resolved_archive_file = cached_file(
                            pretrained_model_name_or_path,
                            _add_variant(WEIGHTS_INDEX_NAME, variant),
                            **cached_file_kwargs,
                        )
                        if resolved_archive_file is not None:
                            is_sharded = True
                    if resolved_archive_file is None:
                        # Otherwise, maybe there is a TF or Flax model file.  We try those to give a helpful error
                        # message.
                        has_file_kwargs = {
                            "revision": revision,
                            "proxies": proxies,
                            "use_auth_token": use_auth_token,
                        }
                        if has_file(pretrained_model_name_or_path, TF2_WEIGHTS_NAME, **has_file_kwargs):
                            raise EnvironmentError(
                                f"{pretrained_model_name_or_path} does not appear to have a file named"
                                f" {_add_variant(WEIGHTS_NAME, variant)} but there is a file for TensorFlow weights."
                                " Use `from_tf=True` to load this model from those weights."
                            )
                        elif has_file(pretrained_model_name_or_path, FLAX_WEIGHTS_NAME, **has_file_kwargs):
                            raise EnvironmentError(
                                f"{pretrained_model_name_or_path} does not appear to have a file named"
                                f" {_add_variant(WEIGHTS_NAME, variant)} but there is a file for Flax weights. Use"
                                " `from_flax=True` to load this model from those weights."
                            )
                        elif variant is not None and has_file(
                            pretrained_model_name_or_path, WEIGHTS_NAME, **has_file_kwargs
                        ):
                            raise EnvironmentError(
                                f"{pretrained_model_name_or_path} does not appear to have a file named"
                                f" {_add_variant(WEIGHTS_NAME, variant)} but there is a file without the variant"
                                f" {variant}. Use `variant=None` to load this model from those weights."
                            )
                        else:
                            raise EnvironmentError(
                                f"{pretrained_model_name_or_path} does not appear to have a file named"
                                f" {_add_variant(WEIGHTS_NAME, variant)}, {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME} or"
                                f" {FLAX_WEIGHTS_NAME}."
                            )
                except EnvironmentError:
                    # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
                    # to the original exception.
                    raise
                except Exception:
                    # For any other exception, we throw a generic error.
                    raise EnvironmentError(
                        f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it"
                        " from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                        f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
                        f" directory containing a file named {_add_variant(WEIGHTS_NAME, variant)},"
                        f" {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME} or {FLAX_WEIGHTS_NAME}."
                    )

            if is_local:
                logger.info(f"loading weights file {archive_file}")
                resolved_archive_file = archive_file
            else:
                logger.info(f"loading weights file {filename} from cache at {resolved_archive_file}")
        else:
            resolved_archive_file = None

        # We'll need to download and cache each checkpoint shard if the checkpoint is sharded.
        if is_sharded:
            # rsolved_archive_file becomes a list of files that point to the different checkpoint shards in this case.
            resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(
                pretrained_model_name_or_path,
                resolved_archive_file,
                cache_dir=cache_dir,
                force_download=force_download,
                proxies=proxies,
                resume_download=resume_download,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
                user_agent=user_agent,
                revision=revision,
                subfolder=subfolder,
                _commit_hash=commit_hash,
            )

        # load pt weights early so that we know which dtype to init the model under
        if from_pt:
            if not is_sharded and state_dict is None:
                # Time to load the checkpoint
                state_dict = load_state_dict(resolved_archive_file)

            # set dtype to instantiate the model under:
            # 1. If torch_dtype is not None, we use that dtype
            # 2. If torch_dtype is "auto", we auto-detect dtype from the loaded state_dict, by checking its first
            #    weights entry that is of a floating type - we assume all floating dtype weights are of the same dtype
            # we also may have config.torch_dtype available, but we won't rely on it till v5
            dtype_orig = None

            if torch_dtype is not None:
                if isinstance(torch_dtype, str):
                    if torch_dtype == "auto":
                        if hasattr(config, "torch_dtype") and config.torch_dtype is not None:
                            torch_dtype = config.torch_dtype
                            logger.info(f"Will use torch_dtype={torch_dtype} as defined in model's config object")
                        else:
                            if is_sharded and "dtype" in sharded_metadata:
                                torch_dtype = sharded_metadata["dtype"]
                            elif not is_sharded:
                                torch_dtype = get_state_dict_dtype(state_dict)
                            else:
                                one_state_dict = load_state_dict(resolved_archive_file[0])
                                torch_dtype = get_state_dict_dtype(one_state_dict)
                                del one_state_dict  # free CPU memory
                            logger.info(
                                "Since the `torch_dtype` attribute can't be found in model's config object, "
                                "will use torch_dtype={torch_dtype} as derived from model's weights"
                            )
                    else:
                        raise ValueError(
                            f'`torch_dtype` can be either `torch.dtype` or `"auto"`, but received {torch_dtype}'
                        )
                dtype_orig = cls._set_default_torch_dtype(torch_dtype)

            # Check if `_keep_in_fp32_modules` is not None
            use_keep_in_fp32_modules = (
                (cls._keep_in_fp32_modules is not None) and is_accelerate_available() and torch_dtype == torch.float16
            )
            if (
                (cls._keep_in_fp32_modules is not None)
                and not is_accelerate_available()
                and torch_dtype == torch.float16
            ):
                logger.warning(
                    "For stability purposes, it is recommended to have accelerate installed when using this model in"
                    " torch.float16, please install it with `pip install accelerate`"
                )

            if is_sharded:
                loaded_state_dict_keys = sharded_metadata["all_checkpoint_keys"]
            else:
                loaded_state_dict_keys = list(state_dict.keys())
            if low_cpu_mem_usage or use_keep_in_fp32_modules:
                state_dict = None

        config.name_or_path = pretrained_model_name_or_path

        # Instantiate model.
        init_contexts = [no_init_weights(_enable=_fast_init)]

        if is_deepspeed_zero3_enabled():
            import deepspeed

            logger.info("Detected DeepSpeed ZeRO-3: activating zero.init() for this model")
            init_contexts = [deepspeed.zero.Init(config_dict_or_path=deepspeed_config())] + init_contexts
        elif load_in_8bit or low_cpu_mem_usage:
            init_contexts.append(init_empty_weights())

        with ContextManagers(init_contexts):
            model = cls(config, *model_args, **model_kwargs)

        # Check first if we are `from_pt`
        if use_keep_in_fp32_modules:
            low_cpu_mem_usage = True
            keep_in_fp32_modules = model._keep_in_fp32_modules
        else:
            keep_in_fp32_modules = []

        if load_in_8bit:
            from .utils.bitsandbytes import get_keys_to_not_convert, replace_8bit_linear

            load_in_8bit_skip_modules = quantization_config.llm_int8_skip_modules
            load_in_8bit_threshold = quantization_config.llm_int8_threshold
            load_in_8bit_fp32_cpu_offload = quantization_config.llm_int8_enable_fp32_cpu_offload

            logger.info("Detected 8-bit loading: activating 8-bit loading for this model")

            # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
            if load_in_8bit_skip_modules is None:
                modules_to_not_convert = get_keys_to_not_convert(model)
            else:
                modules_to_not_convert = load_in_8bit_skip_modules

            if not isinstance(modules_to_not_convert, list):
                modules_to_not_convert = [modules_to_not_convert]

            modules_to_not_convert.extend(keep_in_fp32_modules)

            # Extend the modules to not convert to keys that are supposed to be offloaded to `cpu` or `disk`
            if isinstance(device_map, dict) and len(device_map.keys()) > 1:
                keys_on_cpu = [key for key, value in device_map.items() if value in ["disk", "cpu"]]

                if len(keys_on_cpu) > 0 and not load_in_8bit_fp32_cpu_offload:
                    raise ValueError(
                        "If you want to offload some keys to `cpu` or `disk`, you need to set "
                        "`load_in_8bit_fp32_cpu_offload=True`. Note that these modules will not be "
                        " converted to 8-bit but kept in 32-bit."
                    )

                modules_to_not_convert.extend(keys_on_cpu)

            model = replace_8bit_linear(
                model, threshold=load_in_8bit_threshold, modules_to_not_convert=modules_to_not_convert
            )

            # training in 8-bit is only available in 0.37.0+
            model._is_int8_training_enabled = version.parse(
                importlib_metadata.version("bitsandbytes")
            ) >= version.parse("0.37.0")

        if isinstance(device_map, str):
            special_dtypes = {
                name: torch.float32
                for name, _ in model.named_parameters()
                if any(m in name for m in keep_in_fp32_modules)
            }
            if model._no_split_modules is None:
                raise ValueError(f"{model.__class__.__name__} does not support `device_map='{device_map}'` yet.")
            no_split_modules = model._no_split_modules
            if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
                raise ValueError(
                    "If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or "
                    "'sequential'."
                )
            elif device_map in ["balanced", "balanced_low_0"] and get_balanced_memory is None:
                raise ValueError(f"`device_map={device_map}` requires a source install of Accelerate.")

            kwargs = {"no_split_module_classes": no_split_modules, "max_memory": max_memory}
            if "special_dtypes" in inspect.signature(infer_auto_device_map).parameters:
                kwargs["special_dtypes"] = special_dtypes
            elif len(special_dtypes) > 0:
                logger.warn(
                    "This model has some weights that should be kept in higher precision, you need to upgrade "
                    "`accelerate` to properly deal with them (`pip install --upgrade accelerate`)."
                )
            if device_map != "sequential" and get_balanced_memory is not None:
                max_memory = get_balanced_memory(
                    model,
                    dtype=torch_dtype,
                    low_zero=(device_map == "balanced_low_0"),
                    **kwargs,
                )
            # Make sure tied weights are tied before creating the device map.
            model.tie_weights()
            device_map = infer_auto_device_map(model, dtype=torch_dtype if not load_in_8bit else torch.int8, **kwargs)

            if load_in_8bit:
                # The LM head / tied weights or any last module can stay on disk / CPU
                device_map_without_lm_head = {
                    key: device_map[key] for key in device_map.keys() if key not in modules_to_not_convert
                }
                if "cpu" in device_map_without_lm_head.values() or "disk" in device_map_without_lm_head.values():
                    raise ValueError(
                        """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom
                        `device_map` to `from_pretrained`. Check
                        https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu
                        for more details.
                        """
                    )
                del device_map_without_lm_head

        if from_tf:
            if resolved_archive_file.endswith(".index"):
                # Load from a TensorFlow 1.X checkpoint - provided by original authors
                model = cls.load_tf_weights(model, config, resolved_archive_file[:-6])  # Remove the '.index'
            else:
                # Load from our TensorFlow 2.0 checkpoints
                try:
                    from .modeling_tf_pytorch_utils import load_tf2_checkpoint_in_pytorch_model

                    model, loading_info = load_tf2_checkpoint_in_pytorch_model(
                        model, resolved_archive_file, allow_missing_keys=True, output_loading_info=True
                    )
                except ImportError:
                    logger.error(
                        "Loading a TensorFlow model in PyTorch, requires both PyTorch and TensorFlow to be installed."
                        " Please see https://pytorch.org/ and https://www.tensorflow.org/install/ for installation"
                        " instructions."
                    )
                    raise
        elif from_flax:
            try:
                from .modeling_flax_pytorch_utils import load_flax_checkpoint_in_pytorch_model

                model = load_flax_checkpoint_in_pytorch_model(model, resolved_archive_file)
            except ImportError:
                logger.error(
                    "Loading a Flax model in PyTorch, requires both PyTorch and Flax to be installed. Please see"
                    " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for"
                    " installation instructions."
                )
                raise
        elif from_pt:
            # restore default dtype
            if dtype_orig is not None:
                torch.set_default_dtype(dtype_orig)

            (
                model,
                missing_keys,
                unexpected_keys,
                mismatched_keys,
                offload_index,
                error_msgs,
            ) = cls._load_pretrained_model(
                model,
                state_dict,
                loaded_state_dict_keys,  # XXX: rename?
                resolved_archive_file,
                pretrained_model_name_or_path,
                ignore_mismatched_sizes=ignore_mismatched_sizes,
                sharded_metadata=sharded_metadata,
                _fast_init=_fast_init,
                low_cpu_mem_usage=low_cpu_mem_usage,
                device_map=device_map,
                offload_folder=offload_folder,
                offload_state_dict=offload_state_dict,
                dtype=torch_dtype,
                load_in_8bit=load_in_8bit,
                keep_in_fp32_modules=keep_in_fp32_modules,
            )

        model.is_loaded_in_8bit = load_in_8bit

        # make sure token embedding weights are still tied if needed
        model.tie_weights()

        # Set model in evaluation mode to deactivate DropOut modules by default
        model.eval()

        # If it is a model with generation capabilities, attempt to load the generation config
        if model.can_generate():
            try:
                model.generation_config = GenerationConfig.from_pretrained(
                    pretrained_model_name_or_path,
                    cache_dir=cache_dir,
                    force_download=force_download,
                    resume_download=resume_download,
                    proxies=proxies,
                    local_files_only=local_files_only,
                    use_auth_token=use_auth_token,
                    revision=revision,
                    subfolder=subfolder,
                    _from_auto=from_auto_class,
                    _from_pipeline=from_pipeline,
                    **kwargs,
                )
            except (OSError, TypeError):
                logger.info(
                    "Generation config file not found, using a generation config created from the model config."
                )
                pass

        # Dispatch model with hooks on all devices if necessary
        if device_map is not None:
            dispatch_model(model, device_map=device_map, offload_dir=offload_folder, offload_index=offload_index)

        if output_loading_info:
            if loading_info is None:
                loading_info = {
                    "missing_keys": missing_keys,
                    "unexpected_keys": unexpected_keys,
                    "mismatched_keys": mismatched_keys,
                    "error_msgs": error_msgs,
                }
            return model, loading_info

        return model