mirror of
https://github.com/bigscience-workshop/petals
synced 2024-10-31 09:20:41 +00:00
148 lines
8.1 KiB
Markdown
148 lines
8.1 KiB
Markdown
<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), **Falcon** (40B+), **BLOOM** (176B) (or their derivatives), 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" # This one is fine-tuned Llama 2 (70B)
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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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🔏 **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.
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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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💬 **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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- 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.
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- 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**.
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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>
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</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)
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- 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)
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- [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:
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- Launch a private swarm: [guide](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm)
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- Run a custom model: [guide](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals)
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### Benchmarks
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Please see **Section 3.3** of our [paper](https://arxiv.org/pdf/2209.01188.pdf).
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### 🛠️ Contributing
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Please see our [FAQ](https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions#contributing) on contributing.
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### 📜 Citation
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Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers, Max Ryabinin, Younes Belkada, Artem Chumachenko, Pavel Samygin, and Colin Raffel.
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[Petals: Collaborative Inference and Fine-tuning of Large Models.](https://arxiv.org/abs/2209.01188)
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_arXiv preprint arXiv:2209.01188,_ 2022.
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```bibtex
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@article{borzunov2022petals,
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title = {Petals: Collaborative Inference and Fine-tuning of Large Models},
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author = {Borzunov, Alexander and Baranchuk, Dmitry and Dettmers, Tim and Ryabinin, Max and Belkada, Younes and Chumachenko, Artem and Samygin, Pavel and Raffel, Colin},
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journal = {arXiv preprint arXiv:2209.01188},
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year = {2022},
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url = {https://arxiv.org/abs/2209.01188}
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}
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```
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--------------------------------------------------------------------------------
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<p align="center">
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This project is a part of the <a href="https://bigscience.huggingface.co/">BigScience</a> research workshop.
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</p>
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<p align="center">
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<img src="https://petals.dev/bigscience.png" width="150">
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</p>
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