In this article, we will guide you through the process of using the OpenAssistant LLaMa 30B SFT model, which enables you to leverage powerful AI capabilities while respecting the licensing restrictions imposed by Meta AI. Particularly, we’ll walk through how to apply XOR weights to the models, and troubleshoot common issues that may arise along the way.

What You Will Need

• A copy of the original LLaMA model weights (if unavailable, refer to the alternative note below).
• Linux operating system (preferably Ubuntu).
• Python 3.10 installed.

Important Notes Before You Begin

• This guide is tailored for users operating on Linux. Users with Windows operating systems should consider using WSL (Windows Subsystem for Linux).
• Ensure that your LLaMA 30B checkpoint matches the correct MD5 sums provided in this document. If discrepancies occur, the process will fail.
• If you do not have the LLaMA weights, you may try using this model as a base for the XOR conversion but support will not be provided for issues arising from this approach.

Steps to Convert the LLaMA Weights

1. Create a Clean Python Environment

First, let’s create a fresh Python 3.10 virtual environment to keep everything organized.

python3.10 -m venv xor_venv
source xor_venv/bin/activate

2. Clone the Transformers Repository

Next, clone the Transformers repository from GitHub and checkout the tested version.

git clone https://github.com/huggingface/transformers.git
cd transformers
git checkout d04ec99bec8a0b432fc03ed60cea9a1a20ebaf3c

3. Install Required Dependencies

Make sure to install specific versions of dependencies to avoid compatibility issues.

pip install .
pip install torch==1.13.1 accelerate==0.18.0 sentencepiece==0.1.98 protobuf==3.20.1

4. Check Installed Packages

Verify the installed packages to ensure everything is set up correctly.

pip freeze

5. Convert LLaMA Weights

Now you are ready to run the conversion script for the LLaMA weights:

python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir input_path_llama_base --output_dir output_path_llama30b_hf --model_size 30B

6. Verify the Converted Weights

After conversion, check the MD5 sums of the output files to verify correctness.

find . -type f -exec md5sum  +

7. Apply XOR Decoding

Once verified, proceed to apply XOR decoding:

python xor_codec.py oasst-sft-6-llama-30b oasst-sft-6-llama-30b-xor llama30b_hf

8. Final Verification

Finally, run another MD5 checksum check to ensure the final weights have been applied correctly:

find . -type f -exec md5sum  +

Troubleshooting Common Issues

• **Checksum Not Matching**: If your MD5 checksums do not match the expected values, please double-check your input files. Ensure that your original LLaMA weights are correct and that no errors occurred during conversion.
• **Script Errors**: Should you encounter errors during the execution of any scripts, make sure your virtual environment is activated and that all dependencies are installed correctly.
• **Alternative Model Issues**: If you’re using an alternative model instead of the original LLaMA weights, be aware that we do not provide support for problems that may arise from this approach. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Conclusion

By following these steps, you should be able to successfully use the OpenAssistant LLaMa 30B SFT model in your projects. Remember, patience and attention to detail are your best friends during this process!