docs/LLaMA-model.md

LLaMA is a Large Language Model developed by Meta AI.

It was trained on more tokens than previous models. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters.

This guide will cover usage through the official transformers implementation. For 4-bit mode, head over to GPTQ models (4 bit mode) .

Getting the weights

Option 1: pre-converted weights

• Direct download (recommended):

https://huggingface.co/Neko-Institute-of-Science/LLaMA-7B-HF

https://huggingface.co/Neko-Institute-of-Science/LLaMA-13B-HF

https://huggingface.co/Neko-Institute-of-Science/LLaMA-30B-HF

https://huggingface.co/Neko-Institute-of-Science/LLaMA-65B-HF

• Torrent:

https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789

The tokenizer files in the torrent above are outdated, in particular the files called tokenizer_config.json and special_tokens_map.json. Here you can find those files: https://huggingface.co/oobabooga/llama-tokenizer

Option 2: convert the weights yourself

1. Install the protobuf library:

pip install protobuf==3.20.1

2. Use the script below to convert the model in .pth format that you, a fellow academic, downloaded using Meta's official link.

If you have transformers installed in place:

python -m transformers.models.llama.convert_llama_weights_to_hf --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b

Otherwise download convert_llama_weights_to_hf.py first and run:

python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b

3. Move the llama-7b folder inside your text-generation-webui/models folder.

Starting the web UI

python server.py --model llama-7b