Run 100B+ language models at home, BitTorrent-style.
Fine-tuning and inference up to 10x faster than offloading
Generate text using distributed [LLaMA-65B](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md), [BLOOM-176B](https://huggingface.co/bigscience/bloom) or [BLOOMZ-176B](https://huggingface.co/bigscience/bloomz) and fine-tune them for your own tasks:
```python
from petals import AutoDistributedModelForCausalLM
model = AutoDistributedModelForCausalLM.from_pretrained("bigscience/bloom", tuning_mode="ptune", pre_seq_len=16)
# Embeddings & prompts are on your device, BLOOM blocks are distributed across the Internet
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...
# Fine-tuning (updates only prompts or adapters hosted locally)
optimizer = torch.optim.AdamW(model.parameters())
for input_ids, labels in data_loader:
outputs = model.forward(input_ids)
loss = cross_entropy(outputs.logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
```
🚀 Try now in Colab
🔏 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.
### Connect your GPU and increase Petals capacity
Run our [Docker](https://www.docker.com) image (works on Linux, macOS, and Windows with [WSL2](https://learn.microsoft.com/en-us/windows/ai/directml/gpu-cuda-in-wsl)):
```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 bigscience/bloom --port 31330
```
Or run these commands in an [Anaconda](https://www.anaconda.com) env (requires Linux and Python 3.7+):
```bash
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -U petals
python -m petals.cli.run_server bigscience/bloom
```
📚 See [FAQ](https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions#running-a-server) to learn how to configure the server to use multiple GPUs, address common issues, etc.
🔒 Hosting a server does not allow others to run custom code on your computer. Learn more about security [here](https://github.com/bigscience-workshop/petals/wiki/Security,-privacy,-and-AI-safety).
💬 If you have any issues or feedback, let us know on [our Discord server](https://discord.gg/D9MwApKgWa)!
### Check out tutorials, examples, and more
Basic tutorials:
- Getting started: [tutorial](https://colab.research.google.com/drive/1Ervk6HPNS6AYVr3xVdQnY5a-TjjmLCdQ?usp=sharing)
- 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)
- Prompt-tune BLOOM for text semantic classification: [tutorial](https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb)
Useful tools and advanced guides:
- [Chatbot web app](http://chat.petals.ml) (connects to Petals via an HTTP endpoint): [source code](https://github.com/borzunov/chat.petals.ml)
- [Monitor](http://health.petals.ml) for the public swarm: [source code](https://github.com/borzunov/health.petals.ml)
- Launch your own swarm: [guide](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm)
- Run a custom foundation model: [guide](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals)
Learning more:
- Frequently asked questions: [FAQ](https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions)
- In-depth system description: [paper](https://arxiv.org/abs/2209.01188)
📋 If you build an app running BLOOM with Petals, make sure it follows the BLOOM's [terms of use](https://huggingface.co/bigscience/bloom).
## How does it work?
- Petals runs large language models like [BLOOM-176B](https://huggingface.co/bigscience/bloom) **collaboratively** — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning.
- Single-batch inference runs at ≈ 1 sec per step (token) — [up to 10x faster](https://github.com/bigscience-workshop/petals#benchmarks) than offloading, enough for [chatbots](http://chat.petals.ml) 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, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch.
📚 See FAQ
📜 Read paper
## Installation
Here's how to install Petals with [Anaconda](https://www.anaconda.com/products/distribution) on Linux:
```bash
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -U petals
```
If you don't use Anaconda, you can install PyTorch in [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.x or newer for compatility with [bitsandbytes](https://github.com/timDettmers/bitsandbytes).
See the instructions for macOS and Windows, the full requirements, and troubleshooting advice in our [FAQ](https://github.com/bigscience-workshop/petals/wiki/FAQ:-Frequently-asked-questions#running-a-client).
## Benchmarks
The benchmarks below are for BLOOM-176B:
| Network |
Single-batch inference
(steps/s) |
Parallel forward
(tokens/s) |
| Bandwidth |
Round-trip
latency |
Sequence length |
Batch size |
| 128 |
2048 |
1 |
64 |
| Offloading, max. possible speed on 1x A100 1 |
| 256 Gbit/s |
|
0.18 |
0.18 |
2.7 |
170.3 |
| 128 Gbit/s |
|
0.09 |
0.09 |
2.4 |
152.8 |
| Petals on 14 heterogeneous servers across Europe and North America 2 |
| Real world |
0.83 |
0.79 |
32.6 |
179.4 |
| Petals on 3 servers, with one A100 each 3 |
| 1 Gbit/s |
< 5 ms |
1.71 |
1.54 |
70.0 |
253.6 |
| 100 Mbit/s |
< 5 ms |
1.66 |
1.49 |
56.4 |
182.0 |
| 100 Mbit/s |
100 ms |
1.23 |
1.11 |
19.7 |
112.2 |
1 **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.
2 **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.
3 **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}
}
```
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This project is a part of the BigScience research workshop.
