|
|
<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>
|
|
|
|
|
|
**Warning: Llama 3.1 support is still under construction!** the latest models require custom RoPE configuration that we do not have in Petals yet; we will update the code to fix that within a day.**
|
|
|
|
|
|
Generate text with distributed **Llama (1-3)** (70B), **Falcon** (40B+), **BLOOM** (176B) (or their derivatives), 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 = "petals-team/StableBeluga2" # This one is fine-tuned Llama 2 (70B)
|
|
|
|
|
|
# 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>
|
|
|
|
|
|
🔏 **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.
|
|
|
|
|
|
🦙 **Want to run Llama 2?** Request access to its weights at the ♾️ [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and 🤗 [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), then run `huggingface-cli login` in the terminal before loading the model. Or just try it in our [chatbot app](https://chat.petals.dev).
|
|
|
|
|
|
💬 **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 check out [available models](https://health.petals.dev) and help serving one of them! As an example, here is how to host a part of [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2) on your GPU:
|
|
|
|
|
|
🐧 **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 petals-team/StableBeluga2
|
|
|
```
|
|
|
|
|
|
🪟 **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 petals-team/StableBeluga2
|
|
|
```
|
|
|
|
|
|
🍏 **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 petals-team/StableBeluga2
|
|
|
```
|
|
|
|
|
|
<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>
|
|
|
|
|
|
💬 **Any questions?** Ping us in [our Discord](https://discord.gg/X7DgtxgMhc)!
|
|
|
|
|
|
🦙 **Want to host Llama 2?** Request access to its weights at the ♾️ [Meta AI website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and 🤗 [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), generate an 🔑 [access token](https://huggingface.co/settings/tokens), then add `--token YOUR_TOKEN_HERE` to the `python -m petals.cli.run_server` command.
|
|
|
|
|
|
🔒 **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).
|
|
|
|
|
|
🏆 **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.
|
|
|
|
|
|
### 📜 Citation
|
|
|
|
|
|
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)
|
|
|
_arXiv preprint arXiv:2209.01188,_ 2022.
|
|
|
|
|
|
```bibtex
|
|
|
@article{borzunov2022petals,
|
|
|
title = {Petals: Collaborative Inference and Fine-tuning of Large Models},
|
|
|
author = {Borzunov, Alexander and Baranchuk, Dmitry and Dettmers, Tim and Ryabinin, Max and Belkada, Younes and Chumachenko, Artem and Samygin, Pavel and Raffel, Colin},
|
|
|
journal = {arXiv preprint arXiv:2209.01188},
|
|
|
year = {2022},
|
|
|
url = {https://arxiv.org/abs/2209.01188}
|
|
|
}
|
|
|
```
|
|
|
|
|
|
--------------------------------------------------------------------------------
|
|
|
|
|
|
<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>
|