petals

Run large language models at home, BitTorrent-style.
Fine-tuning and inference up to 10x faster than offloading

Generate text with distributed Llama 3.1 (up to 405B), Mixtral (8x22B), Falcon (40B+) or BLOOM (176B) and fine‑tune them for your own tasks — right from your desktop computer or Google Colab:

from transformers import AutoTokenizer
from petals import AutoDistributedModelForCausalLM

# Choose any model available at https://health.petals.dev
model_name = "meta-llama/Meta-Llama-3.1-405B-Instruct"

# Connect to a distributed network hosting model layers
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoDistributedModelForCausalLM.from_pretrained(model_name)

# Run the model as if it were on your computer
inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"]
outputs = model.generate(inputs, max_new_tokens=5)
print(tokenizer.decode(outputs[0]))  # A cat sat on a mat...

🚀  Try now in Colab

🦙 Want to run Llama? Request access to its weights, then run huggingface-cli login in the terminal before loading the model. Or just try it in our chatbot app.

🔏 Privacy. Your data will be processed with the help of other people in the public swarm. Learn more about privacy here. For sensitive data, you can set up a private swarm among people you trust.

💬 Any questions? Ping us in our Discord!

Connect your GPU and increase Petals capacity

Petals is a community-run system — we rely on people sharing their GPUs. You can help serving one of the available models or host a new model from 🤗 Model Hub!

As an example, here is how to host a part of Llama 3.1 (405B) Instruct on your GPU:

🦙 Want to host Llama? Request access to its weights, then run huggingface-cli login in the terminal before loading the model.

🐧 Linux + Anaconda. Run these commands for NVIDIA GPUs (or follow this for AMD):

conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
pip install git+https://github.com/bigscience-workshop/petals
python -m petals.cli.run_server meta-llama/Meta-Llama-3.1-405B-Instruct

🪟 Windows + WSL. Follow this guide on our Wiki.

🐋 Docker. Run our Docker image for NVIDIA GPUs (or follow this for AMD):

sudo docker run -p 31330:31330 --ipc host --gpus all --volume petals-cache:/cache --rm \
    learningathome/petals:main \
    python -m petals.cli.run_server --port 31330 meta-llama/Meta-Llama-3.1-405B-Instruct

🍏 macOS + Apple M1/M2 GPU. Install Homebrew, then run these commands:

brew install python
python3 -m pip install git+https://github.com/bigscience