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<p align="center">
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<img src="https://i.imgur.com/7eR7Pan.png" width="400"><br>
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Run large language models at home, BitTorrent-style.<br>
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Fine-tuning and inference <a href="https://github.com/bigscience-workshop/petals#benchmarks">up to 10x faster</a> than offloading
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<br><br>
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<a href="https://pypi.org/project/petals/"><img src="https://img.shields.io/pypi/v/petals.svg?color=green"></a>
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<a href="https://discord.gg/tfHfe8B34k"><img src="https://img.shields.io/discord/865254854262652969?label=discord&logo=discord&logoColor=white"></a>
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<br>
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</p>
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Generate text with distributed **Llama 2 (70B)**, **Stable Beluga 2**, **Guanaco-65B** or **BLOOM-176B** and fine‑tune them for your own tasks — right from your desktop computer or Google Colab:
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```python
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from transformers import AutoTokenizer
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from petals import AutoDistributedModelForCausalLM
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# Choose any model available at https://health.petals.dev
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model_name = "petals-team/StableBeluga2"
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# Connect to a distributed network hosting model layers
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoDistributedModelForCausalLM.from_pretrained(model_name)
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# Run the model as if it were on your computer
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inputs = tokenizer("A cat sat", return_tensors="pt")["input_ids"]
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outputs = model.generate(inputs, max_new_tokens=5)
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print(tokenizer.decode(outputs[0])) # A cat sat on a mat...
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```
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<p align="center">
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🚀 <b><a href="https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing">Try now in Colab</a></b>
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</p>
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🦙 **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).
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🔏 **Privacy.** Your data will be processed by 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.
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💬 **Any questions?** Ping us in [our Discord](https://discord.gg/KdThf2bWVU)!
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## Connect your GPU and increase Petals capacity
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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:
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🐧 **Linux + Anaconda.** Run these commands for NVIDIA GPUs (or follow [this](https://github.com/bigscience-workshop/petals/wiki/Running-on-AMD-GPU) for AMD):
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```bash
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conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
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pip install git+https://github.com/bigscience-workshop/petals
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python -m petals.cli.run_server petals-team/StableBeluga2
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```
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🪟 **Windows + WSL.** Follow [this guide](https://github.com/bigscience-workshop/petals/wiki/Run-Petals-server-on-Windows) on our Wiki.
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🐋 **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):
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```bash
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sudo docker run -p 31330:31330 --ipc host --gpus all --volume petals-cache:/cache --rm \
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learningathome/petals:main \
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python -m petals.cli.run_server --port 31330 petals-team/StableBeluga2
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```
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🍏 **macOS + Apple M1/M2 GPU.** Install [Homebrew](https://brew.sh/), then run these commands:
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```bash
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brew install python
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python3 -m pip install git+https://github.com/bigscience-workshop/petals
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python3 -m petals.cli.run_server petals-team/StableBeluga2
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```
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<p align="center">
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📚 <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.)
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</p>
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💬 **Any questions?** Ping us in [our Discord](https://discord.gg/X7DgtxgMhc)!
|
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|
🦙 **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.
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🔒 **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).
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🏆 **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`.
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## How does it work?
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- Petals runs large language models like [Llama](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) and [BLOOM](https://huggingface.co/bigscience/bloom) **collaboratively** — you load a small part of the model, then join people serving the other parts to run inference or fine-tuning.
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- Single-batch inference runs at **up to 6 steps/sec** for **Llama 2** (70B) and ≈ 1 step/sec for BLOOM-176B. This is [up to 10x faster](https://github.com/bigscience-workshop/petals#benchmarks) than offloading, enough to build [chatbots](https://chat.petals.dev) and other interactive apps. Parallel inference reaches hundreds of tokens/sec.
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|
- Beyond classic language model APIs — 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.
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|
<p align="center">
|
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|
<img src="https://i.imgur.com/RTYF3yW.png" width="800">
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|
</p>
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|
<p align="center">
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|
📜 <b><a href="https://arxiv.org/pdf/2209.01188.pdf">Read paper</a></b>
|
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|
📚 <b><a href="https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions">See FAQ</a></b>
|
|
|
</p>
|
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|
|
## 📚 Tutorials, examples, and more
|
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|
Basic tutorials:
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|
|
- 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)
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|
|
- 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)
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|
Useful tools:
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|
|
- [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)
|
|
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|
|
|
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
|
|
|
|
|
|
The benchmarks below are for BLOOM-176B:
|
|
|
|
|
|
<table align="center">
|
|
|
<tr>
|
|
|
<th colspan="2">Network</th>
|
|
|
<th colspan="2">Single-batch inference<br>(steps/s)</th>
|
|
|
<th colspan="2">Parallel forward<br>(tokens/s)</th>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th rowspan="2">Bandwidth</th>
|
|
|
<th rowspan="2">Round-trip<br>latency</th>
|
|
|
<th colspan="2">Sequence length</th>
|
|
|
<th colspan="2">Batch size</th>
|
|
|
</tr>
|
|
|
<tr align="center">
|
|
|
<td>128</td>
|
|
|
<td>2048</td>
|
|
|
<td>1</td>
|
|
|
<td>64</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th colspan="6">Offloading, max. possible speed on 1x A100 <sup>1</sup></th>
|
|
|
</tr>
|
|
|
<tr align="center">
|
|
|
<td>256 Gbit/s</td>
|
|
|
<td></td>
|
|
|
<td>0.18</td>
|
|
|
<td>0.18</td>
|
|
|
<td>2.7</td>
|
|
|
<td>170.3</td>
|
|
|
</tr>
|
|
|
<tr align="center">
|
|
|
<td>128 Gbit/s</td>
|
|
|
<td></td>
|
|
|
<td>0.09</td>
|
|
|
<td>0.09</td>
|
|
|
<td>2.4</td>
|
|
|
<td>152.8</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th colspan="6">Petals on 14 heterogeneous servers across Europe and North America <sup>2</sup></th>
|
|
|
</tr>
|
|
|
<tr align="center">
|
|
|
<td colspan="2">Real world</td>
|
|
|
<td>0.83</td>
|
|
|
<td>0.79</td>
|
|
|
<td>32.6</td>
|
|
|
<td>179.4</td>
|
|
|
</tr>
|
|
|
<tr>
|
|
|
<th colspan="6">Petals on 3 servers, with one A100 each <sup>3</sup></th>
|
|
|
</tr>
|
|
|
<tr align="center">
|
|
|
<td>1 Gbit/s</td>
|
|
|
<td>< 5 ms</td>
|
|
|
<td>1.71</td>
|
|
|
<td>1.54</td>
|
|
|
<td>70.0</td>
|
|
|
<td>253.6</td>
|
|
|
</tr>
|
|
|
<tr align="center">
|
|
|
<td>100 Mbit/s</td>
|
|
|
<td>< 5 ms</td>
|
|
|
<td>1.66</td>
|
|
|
<td>1.49</td>
|
|
|
<td>56.4</td>
|
|
|
<td>182.0</td>
|
|
|
</tr>
|
|
|
<tr align="center">
|
|
|
<td>100 Mbit/s</td>
|
|
|
<td>100 ms</td>
|
|
|
<td>1.23</td>
|
|
|
<td>1.11</td>
|
|
|
<td>19.7</td>
|
|
|
<td>112.2</td>
|
|
|
</tr>
|
|
|
</table>
|
|
|
|
|
|
<sup>1</sup> **An upper bound for offloading performance.** We base our offloading numbers on the best possible hardware setup for offloading: CPU RAM offloading via PCIe 4.0 with 16 PCIe lanes per GPU and PCIe switches for pairs of GPUs. We assume zero latency for the upper bound estimation. In 8-bit, the model uses 1 GB of memory per billion parameters. PCIe 4.0 with 16 lanes has a throughput of 256 Gbit/s, so offloading 176B parameters takes 5.5 seconds. The throughput is twice as slow (128 Gbit/s) if we have two GPUs behind the same PCIe switch.
|
|
|
|
|
|
<sup>2</sup> **A real-world distributed setting** with 14 servers holding 2× RTX 3060, 4× 2080Ti, 2× 3090, 2× A4000, and 4× A5000 GPUs. These are personal servers and servers from university labs, spread across Europe and North America and connected to the Internet at speeds of 100–1000 Mbit/s. 4 servers operate from under firewalls.
|
|
|
|
|
|
<sup>3</sup> **An optimistic setup** that requires least communication. The client nodes have 8 CPU cores and no GPU.
|
|
|
|
|
|
We provide more evaluations and discuss these results in more detail in **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>
|