> ## Documentation Index
> Fetch the complete documentation index at: https://training-docs.cerebras.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Convert From Hugging Face

> Learn how to convert Hugging Face models to the Cerebras Model Zoo format, enabling seamless deployment and optimization on Cerebras's advanced hardware.

This guide uses the `BertForPretraining` class as an example. You'll download the checkpoint and config files, convert the checkpoint, verify the output, and then use the newly converted model.

## Download Checkpoint and Config Files

First, you need to download the model’s checkpoint and configuration files from Hugging Face. 

Here’s how you can do it for `BertForPreTraining`:

```python theme={null}
from transformers import BertForPreTraining

# Replace 'bert-base-uncased' with the model you are interested in
model = BertForPreTraining.from_pretrained("bert-base-uncased")
model.save_pretrained("bert_checkpoint")
```

This code will save two files in the `bert_checkpoint` directory:

* config.json: The model’s configuration file.

* pytorch\_model.bin: The model’s weights.

## Convert Checkpoint to Cerebras Format

Now that you have the necessary files, you can convert them to a format compatible with the Cerebras Model Zoo. Use the provided conversion script in the Cerebras Model Zoo toolkit. Here’s the command:

```python theme={null}
# Navigate to the directory containing the convert_checkpoint.py script
cd <modelzoo path>/modelzoo/common/model_utils/

# Run the conversion
python convert_checkpoint.py convert \
   --model bert \  # Specify the model type
   --src-fmt hf \  # Source format (Hugging Face)
   --tgt-fmt cs-2.0 \  # Target format (Cerebras)
   bert_checkpoint/pytorch_model.bin \  # Input checkpoint file
   --config bert_checkpoint/config.json  # Configuration file

```

Replace `<modelzoo path>` with the actual path to the Model Zoo directory on your system.

### Verify the Output

After running the conversion script, check the `bert_checkpoint` directory for the output files:

* `pytorch_model_to_cs-2.0.mdl`: The converted model checkpoint.

* `config_to_cs-2.0.yaml`: The converted configuration file.

## Use the Converted Model

With the converted files, you can now use the Cerebras Model Zoo tools and workflows to further train or deploy the model on Cerebras hardware.

<Note>
  * If you’re converting a different model (not BERT), replace `--model bert` with the appropriate model flag.

  * If the model you’re converting has a specific variant or is a fine-tuning model, ensure you’re using the correct converter and flags as per the Cerebras documentation.
</Note>