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# Models

The base classes [`PreTrainedModel`], [`TFPreTrainedModel`], and
[`FlaxPreTrainedModel`] implement the common methods for loading/saving a model either from a local
file or directory, or from a pretrained model configuration provided by the library (downloaded from HuggingFace's AWS
S3 repository).

[`PreTrainedModel`] and [`TFPreTrainedModel`] also implement a few methods which
are common among all the models to:

- resize the input token embeddings when new tokens are added to the vocabulary
- prune the attention heads of the model.

The other methods that are common to each model are defined in [`~modeling_utils.ModuleUtilsMixin`]
(for the PyTorch models) and [`~modeling_tf_utils.TFModuleUtilsMixin`] (for the TensorFlow models) or
for text generation, [`~generation_utils.GenerationMixin`] (for the PyTorch models),
[`~generation_tf_utils.TFGenerationMixin`] (for the TensorFlow models) and
[`~generation_flax_utils.FlaxGenerationMixin`] (for the Flax/JAX models).


## PreTrainedModel

[[autodoc]] PreTrainedModel
    - push_to_hub
    - all

<a id='from_pretrained-torch-dtype'></a>

### Model Instantiation dtype

Under Pytorch a model normally gets instantiated with `torch.float32` format. This can be an issue if one tries to
load a model whose weights are in fp16, since it'd require twice as much memory. To overcome this limitation, you can
either explicitly pass the desired `dtype` using `torch_dtype` argument:

```python
model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype=torch.float16)
```

or, if you want the model to always load in the most optimal memory pattern, you can use the special value `"auto"`,
and then `dtype` will be automatically derived from the model's weights:

```python
model = T5ForConditionalGeneration.from_pretrained("t5", torch_dtype="auto")
```

Models instantiated from scratch can also be told which `dtype` to use with:

```python
config = T5Config.from_pretrained("t5")
model = AutoModel.from_config(config)
```

Due to Pytorch design, this functionality is only available for floating dtypes.



## ModuleUtilsMixin

[[autodoc]] modeling_utils.ModuleUtilsMixin

## TFPreTrainedModel

[[autodoc]] TFPreTrainedModel
    - push_to_hub
    - all

## TFModelUtilsMixin

[[autodoc]] modeling_tf_utils.TFModelUtilsMixin

## FlaxPreTrainedModel

[[autodoc]] FlaxPreTrainedModel
    - push_to_hub
    - all

## Pushing to the Hub

[[autodoc]] utils.PushToHubMixin

## Sharded checkpoints

[[autodoc]] modeling_utils.load_sharded_checkpoint
