petals
<p align="center"> <img src="https://i.imgur.com/7eR7Pan.png" width="400"><br> Run large language models at home, BitTorrent-style.<br> Fine-tuning and inference <a href="https://github.com/bigscience-workshop/petals#benchmarks">up to 10x faster</a> than offloading <br><br> <a href="https://pypi.org/project/petals/"><img src="https://img.shields.io/pypi/v/petals.svg?color=green"></a> <a href="https://discord.gg/tfHfe8B34k"><img src="https://img.shields.io/discord/865254854262652969?label=discord&logo=discord&logoColor=white"></a> <br> </p> 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: ```python 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... ``` <p align="center"> 🚀 <b><a href="https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing">Try now in Colab</a></b> </p> 🦙 **Want to run Llama?** [Request access](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) to its weights, then run `huggingface-cli login` in the terminal before loading the model. Or just try it in our [chatbot app](https://chat.petals.dev). 🔏 **Privacy.** Your data will be processed with the help of other people in the public swarm. Learn more about privacy [here](https://github.com/bigscience-workshop/petals/wiki/Security,-privacy,-and-AI-safety). For sensitive data, you can set up a [private swarm](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm) among people you trust. 💬 **Any questions?** Ping us in [our Discord](https://discord.gg/KdThf2bWVU)! ## 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](https://health.petals.dev) or host a new model from 🤗 [Model Hub](https://huggingface.co/models)! As an example, here is how to host a part of [Llama 3.1 (405B) Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) on your GPU: 🦙 **Want to host Llama?** [Request access](https://huggingface.co/meta-llama/Meta-Llama-3.1-405B-Instruct) 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](https://github.com/bigscience-workshop/petals/wiki/Running-on-AMD-GPU) for AMD): ```bash 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](https://github.com/bigscience-workshop/petals/wiki/Run-Petals-server-on-Windows) on our Wiki. 🐋 **Docker.** Run our [Docker](https://www.docker.com) image for NVIDIA GPUs (or follow [this](https://github.com/bigscience-workshop/petals/wiki/Running-on-AMD-GPU) for AMD): ```bash 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](https://brew.sh/), then run these commands: ```bash brew install python python3 -m pip install git+https://github.com/bigscience-workshop/petals python3 -m petals.cli.run_server meta-llama/Meta-Llama-3.1-405B-Instruct ``` <p align="center"> 📚 <b><a href="https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions#running-a-server">Learn more</a></b> (how to use multiple GPUs, start the server on boot, etc.) </p> 🔒 **Security.** Hosting a server does not allow others to run custom code on your computer. Learn more [here](https://github.com/bigscience-workshop/petals/wiki/Security,-privacy,-and-AI-safety). 💬 **Any questions?** Ping us in [our Discord](https://discord.gg/X7DgtxgMhc)! 🏆 **Thank you!** Once you load and host 10+ blocks, we can show your name or link on the [swarm monitor](https://health.petals.dev) as a way to say thanks. You can specify them with `--public_name YOUR_NAME`. ## How does it work? - You load a small part of the model, then join a [network](https://health.petals.dev) of people serving the other parts. Single‑batch inference runs at up to **6 tokens/sec** for **Llama 2** (70B) and up to **4 tokens/sec** for **Falcon** (180B) — enough for [chatbots](https://chat.petals.dev) and interactive apps. - You can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of **PyTorch** and **🤗 Transformers**. <p align="center"> <img src="https://i.imgur.com/RTYF3yW.png" width="800"> </p> <p align="center"> 📜 <b><a href="https://arxiv.org/pdf/2209.01188.pdf">Read paper</a></b> 📚 <b><a href="https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions">See FAQ</a></b> </p> ## 📚 Tutorials, examples, and more Basic tutorials: - Getting started: [tutorial](https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing) - Prompt-tune Llama-65B for text semantic classification: [tutorial](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb) - Prompt-tune BLOOM to create a personified chatbot: [tutorial](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-personachat.ipynb) Useful tools: - [Chatbot web app](https://chat.petals.dev) (connects to Petals via an HTTP/WebSocket endpoint): [source code](https://github.com/petals-infra/chat.petals.dev) - [Monitor](https://health.petals.dev) for the public swarm: [source code](https://github.com/petals-infra/health.petals.dev) Advanced guides: - Launch a private swarm: [guide](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm) - Run a custom model: [guide](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals) ### Benchmarks Please see **Section 3.3** of our [paper](https://arxiv.org/pdf/2209.01188.pdf). ### 🛠️ Contributing Please see our [FAQ](https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions#contributing) on contributing. ### 📜 Citations Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, and Colin Raffel. [Petals: Collaborative Inference and Fine-tuning of Large Models.](https://arxiv.org/abs/2209.01188) _Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)._ 2023. ```bibtex @inproceedings{borzunov2023petals, title = {Petals: Collaborative Inference and Fine-tuning of Large Models}, author = {Borzunov, Alexander and Baranchuk, Dmitry and Dettmers, Tim and Riabinin, Maksim and Belkada, Younes and Chumachenko, Artem and Samygin, Pavel and Raffel, Colin}, booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)}, pages = {558--568}, year = {2023}, url = {https://arxiv.org/abs/2209.01188} } ``` Alexander Borzunov, Max Ryabinin, Artem Chumachenko, Dmitry Baranchuk, Tim Dettmers, Younes Belkada, Pavel Samygin, and Colin Raffel. [Distributed inference and fine-tuning of large language models over the Internet.](https://arxiv.org/abs/2312.08361) _Advances in Neural Information Processing Systems_ 36 (2023). ```bibtex @inproceedings{borzunov2023distributed, title = {Distributed inference and fine-tuning of large language models over the {I}nternet}, author = {Borzunov, Alexander and Ryabinin, Max and Chumachenko, Artem and Baranchuk, Dmitry and Dettmers, Tim and Belkada, Younes and Samygin, Pavel and Raffel, Colin}, booktitle = {Advances in Neural Information Processing Systems}, volume = {36}, pages = {12312--12331}, year = {2023}, url = {https://arxiv.org/abs/2312.08361} } ``` -------------------------------------------------------------------------------- <p align="center"> This project is a part of the <a href="https://bigscience.huggingface.co/">BigScience</a> research workshop. </p> <p align="center"> <img src="https://petals.dev/bigscience.png" width="150"> </p>