This repository is intended as a minimal, hackable and readable example to load LLaMA (arXiv) models and run inference. In order to download the checkpoints and tokenizer, fill this google form
In a conda env with pytorch / cuda available, run
pip install -r requirements.txt
Then in this repository
pip install -e .
Once your request is approved, you will receive links to download the tokenizer and model files.
Edit the download.sh script with the signed url provided in the email to download the model weights and tokenizer.
The provided example.py can be run on a single or multi-gpu node with torchrun and will output completions for two pre-defined prompts. Using TARGET_FOLDER as defined in download.sh:
torchrun --nproc_per_node MP example.py --ckpt_dir $TARGET_FOLDER/model_size --tokenizer_path $TARGET_FOLDER/tokenizer.model
Different models require different MP values:
| Model | MP |
|---|---|
| 7B | 1 |
| 13B | 2 |
| 33B | 4 |
| 65B | 8 |
Use the gradio demo to play around with inference in an interactive way. Make sure to change the model paths in app.py.
torchrun --nproc_per_node 1 app.py
This demo requires ~16GB GPU memory, but could also run on CPU if you adapt a few lines. See this example for the app in use:
Recording.2023-03-02.225512.mp4
See MODEL_CARD.md
See the LICENSE file.