transformers/docs/source/en/converting_tensorflow_models.mdx

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# Converting From Tensorflow Checkpoints

A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints to models
that can be loaded using the `from_pretrained` methods of the library.

<Tip>

Since 2.3.0 the conversion script is now part of the transformers CLI (**transformers-cli**) available in any
transformers >= 2.3.0 installation.

The documentation below reflects the **transformers-cli convert** command format.

</Tip>

## BERT

You can convert any TensorFlow checkpoint for BERT (in particular [the pre-trained models released by Google](https://github.com/google-research/bert#pre-trained-models)) in a PyTorch save file by using the
[convert_bert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert/convert_bert_original_tf_checkpoint_to_pytorch.py) script.

This CLI takes as input a TensorFlow checkpoint (three files starting with `bert_model.ckpt`) and the associated
configuration file (`bert_config.json`), and creates a PyTorch model for this configuration, loads the weights from
the TensorFlow checkpoint in the PyTorch model and saves the resulting model in a standard PyTorch save file that can
be imported using `from_pretrained()` (see example in [quicktour](quicktour) , [run_glue.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_glue.py) ).

You only need to run this conversion script **once** to get a PyTorch model. You can then disregard the TensorFlow
checkpoint (the three files starting with `bert_model.ckpt`) but be sure to keep the configuration file (\
`bert_config.json`) and the vocabulary file (`vocab.txt`) as these are needed for the PyTorch model too.

To run this specific conversion script you will need to have TensorFlow and PyTorch installed (`pip install tensorflow`). The rest of the repository only requires PyTorch.

Here is an example of the conversion process for a pre-trained `BERT-Base Uncased` model:

```bash
export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12

transformers-cli convert --model_type bert \
  --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
  --config $BERT_BASE_DIR/bert_config.json \
  --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
```

You can download Google's pre-trained models for the conversion [here](https://github.com/google-research/bert#pre-trained-models).

## ALBERT

Convert TensorFlow model checkpoints of ALBERT to PyTorch using the
[convert_albert_original_tf_checkpoint_to_pytorch.py](https://github.com/huggingface/transformers/tree/main/src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py) script.

The CLI takes as input a TensorFlow checkpoint (three files starting with `model.ckpt-best`) and the accompanying
configuration file (`albert_config.json`), then creates and saves a PyTorch model. To run this conversion you will
need to have TensorFlow and PyTorch installed.

Here is an example of the conversion process for the pre-trained `ALBERT Base` model:

```bash
export ALBERT_BASE_DIR=/path/to/albert/albert_base

transformers-cli convert --model_type albert \
  --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
  --config $ALBERT_BASE_DIR/albert_config.json \
  --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
```

You can download Google's pre-trained models for the conversion [here](https://github.com/google-research/albert#pre-trained-models).

## OpenAI GPT

Here is an example of the conversion process for a pre-trained OpenAI GPT model, assuming that your NumPy checkpoint
save as the same format than OpenAI pretrained model (see [here](https://github.com/openai/finetune-transformer-lm)\
)

```bash
export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights

transformers-cli convert --model_type gpt \
  --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
  [--config OPENAI_GPT_CONFIG] \
  [--finetuning_task_name OPENAI_GPT_FINETUNED_TASK] \
```

## OpenAI GPT-2

Here is an example of the conversion process for a pre-trained OpenAI GPT-2 model (see [here](https://github.com/openai/gpt-2))

```bash
export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/gpt2/pretrained/weights

transformers-cli convert --model_type gpt2 \
  --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
  [--config OPENAI_GPT2_CONFIG] \
  [--finetuning_task_name OPENAI_GPT2_FINETUNED_TASK]
```

## Transformer-XL

Here is an example of the conversion process for a pre-trained Transformer-XL model (see [here](https://github.com/kimiyoung/transformer-xl/tree/master/tf#obtain-and-evaluate-pretrained-sota-models))

```bash
export TRANSFO_XL_CHECKPOINT_FOLDER_PATH=/path/to/transfo/xl/checkpoint

transformers-cli convert --model_type transfo_xl \
  --tf_checkpoint $TRANSFO_XL_CHECKPOINT_FOLDER_PATH \
  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
  [--config TRANSFO_XL_CONFIG] \
  [--finetuning_task_name TRANSFO_XL_FINETUNED_TASK]
```

## XLNet

Here is an example of the conversion process for a pre-trained XLNet model:

```bash
export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config

transformers-cli convert --model_type xlnet \
  --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
  --config $TRANSFO_XL_CONFIG_PATH \
  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
  [--finetuning_task_name XLNET_FINETUNED_TASK] \
```

## XLM

Here is an example of the conversion process for a pre-trained XLM model:

```bash
export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint

transformers-cli convert --model_type xlm \
  --tf_checkpoint $XLM_CHECKPOINT_PATH \
  --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
 [--config XML_CONFIG] \
 [--finetuning_task_name XML_FINETUNED_TASK]
```

## T5

Here is an example of the conversion process for a pre-trained T5 model:

```bash
export T5=/path/to/t5/uncased_L-12_H-768_A-12

transformers-cli convert --model_type t5 \
  --tf_checkpoint $T5/t5_model.ckpt \
  --config $T5/t5_config.json \
  --pytorch_dump_output $T5/pytorch_model.bin
```