Open-Assistant/README.md

# 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.

## Do you want to try it out?

If you are interested in taking a look at the current state of the project, you
can set up an entire stack needed to run **Open-Assistant**, including the
website, backend, and associated dependent services.

To start the demo, run this in the root directory of the repository:

```sh
docker compose up --build
```

Then, navigate to `http://localhost:3000` (It may take some time to boot up) and
interact with the website.

**Note:** When logging in via email, navigate to `http://localhost:1080` to get
the magic email login link.

## The Plan

We want to get to an initial MVP as fast as possible, by following the 3-steps
outlined in the InstructGPT paper.

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.

## 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.

### Slide Decks

[Vision & Roadmap](https://docs.google.com/presentation/d/1n7IrAOVOqwdYgiYrXc8Sj0He8krn5MVZO_iLkCjTtu0/edit?usp=sharing)

[Important Data Structures](https://docs.google.com/presentation/d/1iaX_nxasVWlvPiSNs0cllR9L_1neZq0RJxd6MFEalUY/edit?usp=sharing)

## How can you 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.

## I’m in! Now what?

[Join the OpenAssistant Contributors Discord Server!](https://ykilcher.com/open-assistant-discord),
this is for work coordination.

[Join the LAION Discord Server!](https://discord.com/invite/mVcgxMPD7e), it has
a dedicated channel and is more public.

[and / or the YK Discord Server](https://ykilcher.com/discord), also has a
dedicated, but not as active, channel.

[Visit the Notion](https://ykilcher.com/open-assistant)

### Taking on Tasks

We have a growing task list
[of issues](https://github.com/LAION-AI/Open-Assistant/issues). Find an issue
that appeals to you and make a comment that you'd like to work on it. Include in
your comment a brief description of how you'll solve the problem and if there
are any open questions you want to discuss. Once a project coordinator has
assigned the issue to you, start working on it.

If the issue is currently unclear but you are interested, please post in Discord
and someone can help clarify the issue with more detail.

**Always Welcome:** Documentation markdowns in `docs/`, docstrings, diagrams of
the system architecture, and other documentation.

### Submitting Work

We're all working on different parts of Open Assistant together. To make
contributions smoothly we recommend the following:

1.  [Fork this project repository](https://docs.github.com/en/get-started/quickstart/fork-a-repo)
    and clone it to your local machine. (Read more
    [About Forks](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/about-forks))
1.  Before working on any changes, try to
    [sync the forked repository](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork)
    to keep it up-to-date with the upstream repository.
1.  Work on a small focused change that only touches on a few files.
1.  Run `pre-commit` and make sure all files have formatting fixed. This
    simplifies life for reviewers.
1.  Package up a small bit of work that solves part of the problem
    [into a Pull Request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork)
    and
    [send it out for review](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/requesting-a-pull-request-review).
1.  If you're lucky, we can merge your change into `main` without any problems.
    If there's changes to files you're working on, resolve them by:
1.  First try rebase as suggested
    [in these instructions](https://timwise.co.uk/2019/10/14/merge-vs-rebase/#should-you-rebase).
1.  If rebase feels too painful, merge as suggested
    [in these instructions](https://timwise.co.uk/2019/10/14/merge-vs-rebase/#should-you-merge).
1.  Once you've resolved any conflicts, finish the review and merge into `main`.
1.  Merge in your change and move onto a new issue or the second step of your
    current issue.

Additionally, if someone is working on an issue that interests you, ask if they
need help on it or would like suggestions on how to approach the issue. If so,
share wildly. If they seem to have a good handle on it, let them work on their
solution until a challenge comes up.

### When does a review finish

A review finishes when all blocking comments are addressed and at least one
owning reviewer has approved the PR. Be sure to acknowledge any non-blocking
comments either by making the request change, explaining why it's not being
addressed now, or filing an issue to handle it later.

## Developer Setup

Work is organized in the
[project board](https://github.com/orgs/LAION-AI/projects/3).

**Anything that is in the `Todo` column and not assigned, is up for grabs.
Meaning we'd be happy for anyone to do these tasks.**

If you want to work on something, assign yourself to it or write a comment that
you want to work on it and what you plan to do.

- To get started with development, if you want to work on the backend, have a
  look at `scripts/backend-development/README.md`.
- If you want to work on any frontend, have a look at
  `scripts/frontend-development/README.md` to make a backend available.

There is also a minimal implementation of a frontend in the `text-frontend`
folder.

We are using Python 3.10 for the backend.

Check out the
[High-Level Protocol Architecture](https://www.notion.so/High-Level-Protocol-Architecture-6f1fd3551da74213b560ead369f132dc)

### Website

The website is built using Next.js and is in the `website` folder.

### Pre-commit

Install `pre-commit` and run `pre-commit install` to install the pre-commit
hooks.

In case you haven't done this, have already committed, and CI is failing, you
can run `pre-commit run --all-files` to run the pre-commit hooks on all files.

### Deployment

Upon making a release on GitHub, all docker images are automatically built and
pushed to ghcr.io. The docker images are tagged with the release version, and
the `latest` tag. Further, the ansible playbook in `ansible/dev.yaml` is run to
automatically deploy the built release to the dev machine.