Open Assistant

:memo: NOTE : OpenAssistant is completed, and the project is now finished. Thank you to everyone who contributed! Check out our blog post for more information. The final published oasst2 dataset can be found on HuggingFace at OpenAssistant/oasst2 Table of Contents What is Open Assistant? Useful Links How To Try It Out The Vision The Plan How You Can Help We believe that by doing this we will create a revolution in innovation in language. In the same way that stable-diffusion helped the world make art and images in new ways we hope Open Assistant can help improve the world by improving language itself. You do not need to run the project locally unless you are contributing to the development process. The website link above will take you to the public website where you can use the data collection app and the chat. If you would like to run the data collection app locally for development, you can set up an entire stack needed to run Open-Assistant , including the website, backend, and associated dependent services, with Docker. Then, navigate to (It may take some time to boot up) and interact with the website. Note: If an issue occurs with the build, please head to the FAQ and check out the entries about Docker. Note: If you would like to run this in a standardized development environment (a "devcontainer") using vscode locally or in a web browser using GitHub Codespaces, you can use the provided .devcontainer folder. You do not need to run the project locally unless you are contributing to the development process. The website link above will take you to the public website where you can use the data collection app and the chat. Also note that the local setup is only for development and is not meant to be used as a local chatbot, unless you know what you are doing. 1. Collect high-quality human generated Instruction-Fulfillment samples (prompt + response), goal 50k. We design a crowdsourced process to collect and reviewed prompts. We do not want to train on flooding/toxic/spam/junk/personal information data. We will have a leaderboard to motivate the community that shows progress and the most active users. Swag will be given to the top-contributors. 2. For each of the collected prompts we will sample multiple completions. Completions of one prompt will then be shown randomly to users to rank them from best to worst. Again this should happen crowd-sourced, e.g. we need to deal with unreliable potentially malicious users. At least multiple votes by independent users have to be collected to measure the overall agreement. The gathered ranking-data will be used to train a reward model. 3. Now follows the RLHF training phase based on the prompts and the reward model. We can then take the resulting model and continue with completion sampling step 2 for a next iteration. All open source projects begin with people like you. Open source is the belief that if we collaborate we can together gift our knowledge and technology to the world for the benefit of humanity.