diff --git a/README.md b/README.md index aa93a43..255e624 100644 --- a/README.md +++ b/README.md @@ -8,15 +8,15 @@

-Generate text with distributed **LLaMA 2 (70B)**, **Stable Beluga 2**, **Guanaco-65B** or **BLOOM-176B** and fine‑tune them for your own tasks — right from your desktop computer or Google Colab: +Generate text with distributed **Llama 2 (70B)**, **Stable Beluga 2**, **Guanaco-65B** or **BLOOM-176B** 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 = "stabilityai/StableBeluga2" +model_name = "petals-team/StableBeluga2" # You can also use "meta-llama/Llama-2-70b-hf", "meta-llama/Llama-2-70b-chat-hf", -# repos with LLaMA-65B, "bigscience/bloom", or "bigscience/bloomz" +# repos with Llama-65B, "bigscience/bloom", or "bigscience/bloomz" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoDistributedModelForCausalLM.from_pretrained(model_name) @@ -31,9 +31,9 @@ print(tokenizer.decode(outputs[0])) # A cat sat on a mat... πŸš€  Try now in Colab

-πŸ¦™ **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 πŸ€— [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). +πŸ¦™ **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 πŸ€— [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). -πŸ“‹ **Terms of use.** Make sure you follow the model license (see [LLaMA 2](https://bit.ly/llama2-license), [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2/blob/main/LICENSE.txt), [LLaMA](https://bit.ly/llama-license), and [BLOOM](https://bit.ly/bloom-license)). +πŸ“‹ **Terms of use.** Make sure you follow the model license (see [Llama 2](https://bit.ly/llama2-license), [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2/blob/main/LICENSE.txt), [Llama](https://bit.ly/llama-license), and [BLOOM](https://bit.ly/bloom-license)). πŸ” **Privacy.** 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. @@ -48,7 +48,7 @@ Petals is a community-run system — we rely on people sharing their GPUs. Y ```bash conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia pip install git+https://github.com/bigscience-workshop/petals -python -m petals.cli.run_server stabilityai/StableBeluga2 +python -m petals.cli.run_server petals-team/StableBeluga2 ``` πŸͺŸ **Windows + WSL.** Follow the guide on our [Wiki](https://github.com/bigscience-workshop/petals/wiki/Run-Petals-server-on-Windows). @@ -57,12 +57,12 @@ python -m petals.cli.run_server stabilityai/StableBeluga2 ```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 --port 31330 stabilityai/StableBeluga2 + python -m petals.cli.run_server --port 31330 petals-team/StableBeluga2 ``` -These commands will host a part of [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2) on your machine. You can also host `meta-llama/Llama-2-70b-hf`, `meta-llama/Llama-2-70b-chat-hf`, repos with LLaMA-65B, `bigscience/bloom`, `bigscience/bloomz`, and other compatible models from πŸ€— [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. +These commands will host a part of [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2) on your machine. You can also host `meta-llama/Llama-2-70b-hf`, `meta-llama/Llama-2-70b-chat-hf`, repos with Llama-65B, `bigscience/bloom`, `bigscience/bloomz`, and other compatible models from πŸ€— [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. -πŸ¦™ **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 πŸ€— [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), generate an πŸ”‘ [access token](https://huggingface.co/settings/tokens), then use this command for `petals.cli.run_server`: +πŸ¦™ **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 πŸ€— [Model Hub](https://huggingface.co/meta-llama/Llama-2-70b-hf), generate an πŸ”‘ [access token](https://huggingface.co/settings/tokens), then use this command for `petals.cli.run_server`: ```bash python -m petals.cli.run_server meta-llama/Llama-2-70b-chat-hf --token YOUR_TOKEN_HERE @@ -79,7 +79,7 @@ python -m petals.cli.run_server meta-llama/Llama-2-70b-chat-hf --token YOUR_TOKE Basic tutorials: - Getting started: [tutorial](https://colab.research.google.com/drive/1uCphNY7gfAUkdDrTx21dZZwCOUDCMPw8?usp=sharing) -- 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) +- 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) - 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) Useful tools and advanced guides: @@ -96,8 +96,8 @@ Learning more: ## How does it work? -- 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** β€” you load a small part of the model, then join 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 to build [chatbots](https://chat.petals.dev) 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** β€” you load a small part of the model, then join 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 to build [chatbots](https://chat.petals.dev) and other interactive apps. Parallel inference reaches hundreds of tokens/sec. - Beyond classic language model APIs β€” 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.

diff --git a/src/petals/__init__.py b/src/petals/__init__.py index 1ef8609..ea64ec9 100644 --- a/src/petals/__init__.py +++ b/src/petals/__init__.py @@ -11,7 +11,7 @@ from petals.models import * from petals.utils import * from petals.utils.logging import initialize_logs as _initialize_logs -__version__ = "2.0.1.post2" +__version__ = "2.1.0" if not os.getenv("PETALS_IGNORE_DEPENDENCY_VERSION"):