Generate text with distributed [LLaMA-65B](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md), [Guanaco](https://huggingface.co/timdettmers/guanaco-65b), [BLOOM-176B](https://huggingface.co/bigscience/bloom), or [BLOOMZ](https://huggingface.co/bigscience/bloomz) and fine-tune them for your own tasks — right from your desktop computer or Google Colab:
Generate text with distributed [LLaMA 2](https://ai.meta.com/llama/) ([70B](https://huggingface.co/meta-llama/Llama-2-70b-hf), [70B-Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)), [LLaMA-65B](https://github.com/facebookresearch/llama/blob/llama_v1/MODEL_CARD.md), [Guanaco-65B](https://huggingface.co/timdettmers/guanaco-65b) or [BLOOM-176B](https://huggingface.co/bigscience/bloom) and fine‑tune them for your own tasks — right from your desktop computer or Google Colab:
```python
from transformers import AutoTokenizer
from petals import AutoDistributedModelForCausalLM
model_name = "enoch/llama-65b-hf" # You can also use "bigscience/bloom" or "bigscience/bloomz"
model_name = "enoch/llama-65b-hf"
# You can also use "meta-llama/Llama-2-70b-hf", "meta-llama/Llama-2-70b-chat-hf",
model = AutoDistributedModelForCausalLM.from_pretrained(model_name)
# Embeddings & prompts are on your device, transformer blocks are distributed across the Internet
@ -25,7 +28,7 @@ print(tokenizer.decode(outputs[0])) # A cat sat on a mat...
🚀 <b><ahref="https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing">Try now in Colab</a></b>
</p>
📋 Make sure you follow the model's terms of use (see [LLaMA](https://bit.ly/llama-license) and [BLOOM](https://bit.ly/bloom-license) licenses). Note that LLaMA is available for non-commercial purposes only, and you have to file a request [here](https://bit.ly/llama-license) to use it in your own projects.
📋 Make sure you follow the model's terms of use (see [LLaMA 2](https://bit.ly/llama2-license), [LLaMA](https://bit.ly/llama-license) and [BLOOM](https://bit.ly/bloom-license) licenses).
🔏 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.
@ -35,7 +38,7 @@ Run these commands in an [Anaconda](https://www.anaconda.com) env (requires Linu
This will host a part of LLaMA-65B with optional [Guanaco](https://huggingface.co/timdettmers/guanaco-65b) adapters on your machine. You can also host `bigscience/bloom`, `bigscience/bloomz`, and other compatible models from 🤗 [Model Hub](https://huggingface.co/models), or [add support](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals) for new model architectures.
This will host a part of LLaMA-65B with optional [Guanaco](https://huggingface.co/timdettmers/guanaco-65b) adapters on your machine. You can also host `meta-llama/Llama-2-70b-hf`, `meta-llama/Llama-2-70b-chat-hf`, `bigscience/bloom`, `bigscience/bloomz`, and other compatible models from 🤗 [Model Hub](https://huggingface.co/models), or [add support](https://github.com/bigscience-workshop/petals/wiki/Run-a-custom-model-with-Petals) for new model architectures.
🔒 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).
@ -74,8 +77,8 @@ Learning more:
## How does it work?
- Petals runs large language models like [LLaMA-65B](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) or [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 3-4 steps/sec for LLaMA-65B and ≈ 1 step/sec for BLOOM-176B — [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.
- 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 team up with people serving the other parts to run inference or fine-tuning.
- 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 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.
<palign="center">
@ -94,7 +97,7 @@ Here's how to install Petals with [Anaconda](https://www.anaconda.com/products/d
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).
Alexander Borzunov
11 months ago
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