- **Petals** runs inference or fine-tunes large language models like [BLOOM-176B](https://huggingface.co/bigscience/bloom) by joining compute resources with people all over the Internet.
- One participant with weak GPU can load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning.
- Inference runs at ≈ 1 sec per step (token) — 10x faster than possible with offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec.
- Beyond classic language model APIs — you can employ any fine-tuning and sampling methods by executing custom paths through the model or accessing its hidden states. This combines the comforts of an API with the flexibility of PyTorch.
Before building your own application that runs a language model with Petals, please make sure that you are familiar with the model's **terms of use, risks, and limitations**. In case of BLOOM, they are described in its [model card](https://huggingface.co/bigscience/bloom) and [license](https://huggingface.co/spaces/bigscience/license).
**If you work with sensitive data, do not use the public swarm.** This is important because it's technically possible for peers serving model layers to recover input data and model outputs, or modify the outputs in a malicious way. Instead, you can [set up a private Petals swarm](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm) hosted by people and organization you trust, who are authorized to process this data.
1.**What's the motivation for people to host model layers in the public swarm?**
People who run inference and fine-tuning themselves get a certain speedup if they host a part of the model locally. Some may be also motivated to "give back" to the community helping them to run the model (similarly to how [BitTorrent](https://en.wikipedia.org/wiki/BitTorrent) users help others by sharing data they have already downloaded).
Since it may be not enough for everyone, we are also working on introducing explicit __incentives__ ("bloom points") for people donating their GPU time to the public swarm. Once this system is ready, people who earned these points will be able to spend them on inference/fine-tuning with higher priority or increased security guarantees, or (maybe) exchange them for other rewards.
2.**Why is the platform named "Petals"?**
"Petals" is a metaphor for people serving different parts of the model. Together, they host the entire language model — [BLOOM](https://huggingface.co/bigscience/bloom).
While our platform focuses on BLOOM now, we aim to support more [foundation models](https://arxiv.org/abs/2108.07258) in future.
This script uses Anaconda to install cuda-enabled PyTorch.
If you don't have anaconda, you can get it from [here](https://www.anaconda.com/products/distribution).
If you don't want anaconda, you can install PyTorch [any other way](https://pytorch.org/get-started/locally/).
If you want to run models with 8-bit weights, please install **PyTorch with CUDA 11** or newer for compatility with [bitsandbytes](https://github.com/timDettmers/bitsandbytes).
__System requirements:__ Petals only supports Linux for now. If you don't have a Linux machine, consider running Petals in Docker (see our [image](https://hub.docker.com/r/learningathome/petals)) or, in case of Windows, in WSL2 ([read more](https://learn.microsoft.com/en-us/windows/ai/directml/gpu-cuda-in-wsl)). CPU is enough to run a client, but you probably need a GPU to run a server efficiently.
To run minimalistic tests, you need to make a local swarm with a small model and some servers. You may find more information about how local swarms work and how to run them in [this tutorial](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm).
The automated tests use a more complex server configuration that can be found [here](https://github.com/bigscience-workshop/petals/blob/main/.github/workflows/run-tests.yaml).
We use [black](https://black.readthedocs.io/en/stable/the_black_code_style/current_style.html) and [isort](https://pycqa.github.io/isort/) for all pull requests.