# Torch API ## Torch API **safetensors.torch.load\_file** [\](https://github.com/huggingface/safetensors/blob/main/src/safetensors/torch.py#L284) ( filename: typing.Union\[str, os.PathLike]device = 'cpu' ) → `Dict[str, torch.Tensor]` Parameters * **filename** (`str`, or `os.PathLike`) — The name of the file which contains the tensors * **device** (`Dict[str, any]`, *optional*, defaults to `cpu`) — The device where the tensors need to be located after load. available options are all regular torch device locations Returns `Dict[str, torch.Tensor]` dictionary that contains name as key, value as `torch.Tensor` Loads a safetensors file into torch format. Example: Copied ``` from safetensors.torch import load_file file_path = "./my_folder/bert.safetensors" loaded = load_file(file_path) ``` **safetensors.torch.load** [\](https://github.com/huggingface/safetensors/blob/main/src/safetensors/torch.py#L314) ( data: bytes ) → `Dict[str, torch.Tensor]` Parameters * **data** (`bytes`) — The content of a safetensors file Returns `Dict[str, torch.Tensor]` dictionary that contains name as key, value as `torch.Tensor` on cpu Loads a safetensors file into torch format from pure bytes. Example: Copied ``` from safetensors.torch import load file_path = "./my_folder/bert.safetensors" with open(file_path, "rb") as f: data = f.read() loaded = load(data) ``` **safetensors.torch.save\_file** [\](https://github.com/huggingface/safetensors/blob/main/src/safetensors/torch.py#L250) ( tensors: typing.Dict\[str, torch.Tensor]filename: typing.Union\[str, os.PathLike]metadata: typing.Union\[typing.Dict\[str, str], NoneType] = None ) → `None` Parameters * **tensors** (`Dict[str, torch.Tensor]`) — The incoming tensors. Tensors need to be contiguous and dense. * **filename** (`str`, or `os.PathLike`)) — The filename we’re saving into. * **metadata** (`Dict[str, str]`, *optional*, defaults to `None`) — Optional text only metadata you might want to save in your header. For instance it can be useful to specify more about the underlying tensors. This is purely informative and does not affect tensor loading. Returns `None` Saves a dictionary of tensors into raw bytes in safetensors format. Example: Copied ``` from safetensors.torch import save_file import torch tensors = {"embedding": torch.zeros((512, 1024)), "attention": torch.zeros((256, 256))} save_file(tensors, "model.safetensors") ``` **safetensors.torch.save** [\](https://github.com/huggingface/safetensors/blob/main/src/safetensors/torch.py#L220) ( tensors: typing.Dict\[str, torch.Tensor]metadata: typing.Union\[typing.Dict\[str, str], NoneType] = None ) → `bytes` Parameters * **tensors** (`Dict[str, torch.Tensor]`) — The incoming tensors. Tensors need to be contiguous and dense. * **metadata** (`Dict[str, str]`, *optional*, defaults to `None`) — Optional text only metadata you might want to save in your header. For instance it can be useful to specify more about the underlying tensors. This is purely informative and does not affect tensor loading. Returns `bytes` The raw bytes representing the format Saves a dictionary of tensors into raw bytes in safetensors format. Example: Copied ``` from safetensors.torch import save import torch tensors = {"embedding": torch.zeros((512, 1024)), "attention": torch.zeros((256, 256))} byte_data = save(tensors) ``` **safetensors.torch.load\_model** [\](https://github.com/huggingface/safetensors/blob/main/src/safetensors/torch.py#L176) ( model: Modulefilename: typing.Union\[str, os.PathLike]strict = True ) → \`(missing, unexpected) Parameters * **model** (`torch.nn.Module`) — The model to load onto. * **filename** (`str`, or `os.PathLike`) — The filename location to load the file from. * **strict** (`bool`, *optional*, defaults to True) — Wether to fail if you’re missing keys or having unexpected ones When false, the function simply returns missing and unexpected names. Returns \`(missing, unexpected) (List\[str], List\[str]) missing`are names in the model which were not modified during loading`unexpected\` are names that are on the file, but weren’t used during the load. Loads a given filename onto a torch model. This method exists specifically to avoid tensor sharing issues which are not allowed in `safetensors`. [More information on tensor sharing](https://huggingface.co/docs/safetensors/torch_shared_tensors) **safetensors.torch.save\_model** [\](https://github.com/huggingface/safetensors/blob/main/src/safetensors/torch.py#L130) ( model: Modulefilename: strmetadata: typing.Union\[typing.Dict\[str, str], NoneType] = Noneforce\_contiguous: bool = True ) Parameters * **model** (`torch.nn.Module`) — The model to save on disk. * **filename** (`str`) — The filename location to save the file * **metadata** (`Dict[str, str]`, *optional*) — Extra information to save along with the file. Some metadata will be added for each dropped tensors. This information will not be enough to recover the entire shared structure but might help understanding things * **force\_contiguous** (`boolean`, *optional*, defaults to True) — Forcing the state\_dict to be saved as contiguous tensors. This has no effect on the correctness of the model, but it could potentially change performance if the layout of the tensor was chosen specifically for that reason. Saves a given torch model to specified filename. This method exists specifically to avoid tensor sharing issues which are not allowed in `safetensors`. [More information on tensor sharing](https://huggingface.co/docs/safetensors/torch_shared_tensors) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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