Overview
Petals is a community-run system that enables distributed inference and fine-tuning of large language models by splitting model layers across multiple machines (BitTorrent-style). It supports models from the Hugging Face Hub and provides tutorials, Docker images, and Colab notebooks for easy experimentation.
Key Features
- Distributed inference: split model computation across a network to enable running large models on commodity hardware.
- Multi-model support: run Llama 3.1, Falcon, BLOOM and other large pretrained models.
- Portable tooling: Docker images, examples, and Colab demos make it easy to get started on Linux, macOS, or WSL.
Use Cases
- Interactive chatbots and research experiments on resource-constrained hardware.
- Collaborative GPU sharing and fine-tuning among volunteers or private swarms.
- Building public or private swarms to host models and improve availability.
Technical Details
- Implemented on PyTorch and Hugging Face Transformers, compatible with existing model weights and tooling.
- Uses pipeline parallelism and network layer distribution to partition model computation.
- Provides Docker support, monitoring tools, and a public swarm health dashboard ( https://health.petals.dev/ ).