Community Model Converter User Guide

This guide provides instructions on how to use the conversion scripts to convert models between Community model and NVIDIA’s NeMo format.

To convert a Hugging Face LLaMA checkpoint into a NeMo checkpoint, use the following command:

            

            
python convert_llama_hf_to_nemo.py>`_ \
 --input_name_or_path <path_to_hf_checkpoints_folder> \
 --output_path <path_to_output_nemo_file>
        

To convert a NeMo checkpoint into a Hugging Face LLaMA checkpoint, you have two options:

1. Generate only the Hugging Face weights:

            

            
python convert_<model>_nemo_to_hf.py>`_ \
--input_name_or_path /path/to/file.nemo or /path/to/extracted_folder \
--output_path /path/to/pytorch_model.bin
        

2. Generate the full Hugging Face model folder:

            

            
python convert_<model>_nemo_to_hf.py>`_ \
--input_name_or_path /path/to/file.nemo or /path/to/extracted_folder \
--output_path /path/to/model_folder \
--hf_input_path /path/to/input_hf_folder \
--hf_output_path /path/to/output_hf_folder
        

Replace <model> with the specific model you are converting.

Use the --cpu-only flag if the model cannot fit in the GPU, such as for Llama2 70b models. Note that using this option will significantly slow down the conversion process.

• --input_name_or_path: Path to the input .nemo file or the Hugging Face model folder.

• --output_path: Path to the output file or folder, depending on the conversion direction.

• --hf_input_path: (Optional) Path to the input Hugging Face model folder.

• --hf_output_path: (Optional) Path to the output Hugging Face model folder.