diff --git a/README.md b/README.md index c5e0166..16b5e8b 100644 --- a/README.md +++ b/README.md @@ -3,17 +3,6 @@ Test Status: ![test status](https://github.com/BatsResearch/alfred/actions/workflows/tests.yml/badge.svg?branch=main) -# Overview - -Alfred is a prototype framework for integrating large pretrained model into programmatic weak supervision pipelines. -Alfred provides an intuitive and user-friendly interface, enabling users to quickly create and refine prompts as supervision sources and interact with large models. -Furthermore, Alfred includes tools for label modeling, allowing the mixed signals from prompted model responses to be combined, distilled and denoised. -Additionally, Alfred enables memory- and computation- intensive models to be run on cloud or computing clusters with optimized batching mechanisms, significantly increasing query throughput. -Alfred aims to reduce annotation cost and time by making efficient use of LLMs, allowing users to make the most of their resources. - -![alt text](assets/examples.png) -![alt text](assets/poster.png) - # News Update - **[[GPT-4V(ision)(https://openai.com/research/gpt-4v-system-card) Support]** Alfred now supports GPT-4V(ision). Use it to streamline your image annotation tasks! For example: @@ -47,6 +36,18 @@ Alfred aims to reduce annotation cost and time by making efficient use of LLMs, claude.chat() ``` + +# Overview + +Alfred is a prototype framework for integrating large pretrained model into programmatic weak supervision pipelines. +Alfred provides an intuitive and user-friendly interface, enabling users to quickly create and refine prompts as supervision sources and interact with large models. +Furthermore, Alfred includes tools for label modeling, allowing the mixed signals from prompted model responses to be combined, distilled and denoised. +Additionally, Alfred enables memory- and computation- intensive models to be run on cloud or computing clusters with optimized batching mechanisms, significantly increasing query throughput. +Alfred aims to reduce annotation cost and time by making efficient use of LLMs, allowing users to make the most of their resources. + +![alt text](assets/examples.png) +![alt text](assets/poster.png) + # Citation If you find Alfred useful, please cite the following work. Thank you!