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 |
This fork of Meta's llama code has a discord bot. First you need to create a discord bot:
- Turn on “Developer mode” in your Discord account.
- Click on “Discord API”.
- In the Developer portal, click on “Applications”. ...
- Name the bot and then click “Create”.
- Go to the “Bot” menu and generate a token using “Add Bot”.
- Generate and copy your token. You need to insert this token in the 'discordtoken.txt' file
After this, you run the bot with the 'runbod.sh' command, or in this way:
torchrun --nproc_per_node 2 bot.py --ckpt_dir data/13B --tokenizer_path .data/tokenizer.model
This will launch the bot using 2XRTX3090 and the 13B model. For different models, adjust the proc_per_node variable (1 for 7B, 2 for 13B, 4 for 30B and 8 for 65B)
- 1. The download.sh script doesn't work on default bash in MacOS X
- 2. Generations are bad!
- 3. CUDA Out of memory errors
- 4. Other languages
LLaMA: Open and Efficient Foundation Language Models -- https://arxiv.org/abs/2302.13971
@article{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
See MODEL_CARD.md
See the LICENSE file.