From 121e8a4756b8dbec7fc98d4d3a6367a29ea8e787 Mon Sep 17 00:00:00 2001 From: Alexander Borzunov Date: Sat, 3 Sep 2022 06:14:28 +0400 Subject: [PATCH] Mention "Petals" in bold in key features --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 78cbef8..6f0644f 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ ## Key features - Run inference or fine-tune large language models like [BLOOM-176B](https://huggingface.co/bigscience/bloom) by joining compute resources with people all over the Internet. No need to have high-end GPUs. -- It's difficult to fit the whole BLOOM-176B into GPU memory [unless](https://twitter.com/Tim_Dettmers/status/1559892918395031552) you have multiple high-end GPUs. Instead, Petals allows to load and serve a small part of the model, then team up with people serving all the other parts to run inference or fine-tuning. +- It's difficult to fit the whole BLOOM-176B into GPU memory [unless](https://twitter.com/Tim_Dettmers/status/1559892918395031552) you have multiple high-end GPUs. Instead, **Petals** allows to load and serve a small part of the model, then team up with people serving all the other parts to run inference or fine-tuning. - This way, one inference step takes ≈ 1 sec — much faster than possible with offloading. Enough for chatbots and other interactive apps. - Beyond traditional language model APIs — you can employ any fine-tuning and sampling methods by executing custom paths through the model or accessing its hidden states. This allows for the comforts of an API with the flexibility of PyTorch.