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?
Open Assistant is a project meant to give everyone access to a great chat based
large language model.
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.
Useful Links
How To Try It Out
Chatting with the AI
The chat frontend is now live here. Log in and
start chatting! Please try to react with a thumbs up or down for the assistant's
responses when chatting.
Contributing to Data Collection
The data collection frontend is now live here. Log
in and start taking on tasks! We want to collect a high volume of quality data.
By submitting, ranking, and labelling model prompts and responses you will be
directly helping to improve the capabilities of Open Assistant.
Running the Development Setup Locally (without chat)
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.
To start the demo, run this in the root directory of the repository (check
this FAQ
if you have problems):
docker compose --profile ci up --build --attach-dependencies
Note: when running on MacOS with an M1 chip you have to use:
DB_PLATFORM=linux/x86_64 docker compose ...
Then, navigate to http://localhost:3000 (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: When logging in via email, navigate to http://localhost:1080 to
get the magic email login link.
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.
Running the Development Setup Locally for Chat
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.
If you do know what you are doing, then see the inference folder for getting
the inference system up and running, or have a look at --profile inference in
addition to --profile ci in the above command.
The Vision
We are not going to stop at replicating ChatGPT. We want to build the assistant
of the future, able to not only write email and cover letters, but do meaningful
work, use APIs, dynamically research information, and much more, with the
ability to be personalized and extended by anyone. And we want to do this in a
way that is open and accessible, which means we must not only build a great
assistant, but also make it small and efficient enough to run on consumer
hardware.
The Plan
We want to get to an initial MVP as fast as possible, by following the 3-steps outlined in the InstructGPT paper
- 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.
- 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.
- 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.
Slide Decks
Vision & Roadmap
Important Data Structures
How You Can Help
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.
Check out our contributing guide to get started.
OpenAssistant 是一个社区项目，旨在打造一款免费、开放的聊天式 AI 助手，类似 ChatGPT，其训练依托众包的指令数据和 RLHF。该项目现已结束：最终的“oasst2”数据集已发布在 Hugging Face 上，此仓库作为这项工作的记录保留下来。