From b58141ef667534a4db4d3aa6905164484361d438 Mon Sep 17 00:00:00 2001 From: Alexander Borzunov Date: Sun, 6 Aug 2023 18:55:22 +0400 Subject: [PATCH] Remove distracting links from readme (#441) --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index f042051..ea7919a 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@

-Generate text with distributed [LLaMA 2 (70B)](https://huggingface.co/meta-llama/Llama-2-70b-hf), [Stable Beluga 2](https://huggingface.co/stabilityai/StableBeluga2), [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: +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 @@ -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** — 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](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** — 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 — 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.