# Prepare Llama models for ExecuTorch

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/)
- [Create a development environment](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-1/)
- [Set up ExecuTorch](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-2/)
- [Understand Llama models](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-3/)
- [Prepare Llama models for ExecuTorch](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-4/)
- [Run the chatbot on Android](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-5/)
- [Build and run the Android chat app](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/how-to-6/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/customer-support-chatbot-with-llama-and-executorch-on-arm-based-mobile-devices/_next-steps/)

## Download and export the Llama 3.2 1B model
To get started with Llama 3, obtain the pre-trained parameters by visiting [Meta’s Llama Downloads](https://llama.meta.com/llama-downloads/) page. Request access by providing your details, then read and accept the Responsible Use Guide. After approval, you receive a license and a download link valid for 24 hours. The Llama 3.2 1B Instruct model is used in this Learning Path, but the same instructions apply to other variants with minimal modification.

Install the `llama-stack` package from `pip`:

```
pip install llama-stack
```

Run the command to download, and paste the download link from the email when prompted:

```
llama model download --source meta --model-id Llama3.2-1B-Instruct
```

When the download finishes, the installation path prints:

```
Successfully downloaded model to /<path-to-home>/.llama/checkpoints/Llama3.2-1B-Instruct
```

Verify by listing the downloaded files:

```
ls $HOME/.llama/checkpoints/Llama3.2-1B-Instruct
```

The output is similar to:

```
checklist.chk    consolidated.00.pth    params.json    tokenizer.model
```

**Working Directory**  
The remaining instructions should be run from the ExecuTorch base directory.

## Export to .pte format
Export the model and generate a `.pte` file. Run the Python command below to export the model to your current directory:

```
python3 -m examples.models.llama.export_llama \
--checkpoint $HOME/.llama/checkpoints/Llama3.2-1B-Instruct/consolidated.00.pth \
--params $HOME/.llama/checkpoints/Llama3.2-1B-Instruct/params.json \
-kv --use_sdpa_with_kv_cache -X --xnnpack-extended-ops -qmode 8da4w \
--group_size 64 -d fp32 \
--metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001, 128006, 128007]}' \
--embedding-quantize 4,32 \
--output_name="llama3_1B_kv_sdpa_xnn_qe_4_64_1024_embedding_4bit.pte" \
--max_seq_length 1024 \
--max_context_length 1024
```

Due to the larger vocabulary size of Llama 3, quantize the embeddings with `--embedding-quantize 4,32` to further reduce model size.

When this command completes, you have a `llama3_1B_kv_sdpa_xnn_qe_4_64_1024_embedding_4bit.pte` file in your working directory. This is the compiled, quantized model ready for on-device deployment via ExecuTorch.

## What you’ve learned and what’s next
You have:
- Downloaded the Llama 3.2 1B Instruct model from Meta
- Exported the model to `.pte` format with quantization optimizations
- Created a model file ready for on-device deployment

The next section builds the Llama runner for Android and demonstrates how to run the chatbot directly on your phone via the command line.