mirror of
https://github.com/wassname/Open-Assistant.git
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Andrew Maguire
b493256b60
Add more prominent/obvious link to docs site. People can easily miss it if it's just in the readme badges.
134 lines
6.2 KiB
Markdown
134 lines
6.2 KiB
Markdown
<h1 align="center">
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<span>Open-Assistant</span>
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<img width="auto" height="50px" src="https://github.com/LAION-AI/Open-Assistant/blob/main/assets/logo_crop.png"/>
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</h1>
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<div align="center">
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<a href="https://github.com/LAION-AI/Open-Assistant/stargazers"></a>
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<a href="https://laion-ai.github.io/Open-Assistant/"></a>
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<a href="https://github.com/LAION-AI/Open-Assistant/actions/workflows/build-frontend.yaml"></a>
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<a href="https://github.com/LAION-AI/Open-Assistant/actions/workflows/pre-commit.yaml"></a>
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<a href="https://github.com/LAION-AI/Open-Assistant/actions/workflows/test-api-contract.yaml"></a>
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<a href="https://github.com/LAION-AI/Open-Assistant/releases"></a>
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</div>
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# Here is our website to collect data:
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[open-assistant.io](https://open-assistant.io)
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(project documentation lives [here](https://laion-ai.github.io/Open-Assistant/))
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# Table of Contents
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- [What is Open Assistant?](#what-is-open-assistant)
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- [Do you want to try it out?](#do-you-want-to-try-it-out)
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- [The Plan](#the-plan)
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- [The Vision](#the-vision)
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- [How can you help?](#how-can-you-help)
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- [I’m in! How do I contribute?](CONTRIBUTING.md)
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---
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## What is Open Assistant?
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<p align="center">
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Open Assistant is a project meant to give everyone access to a great chat based large language model.
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</p>
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We believe that by doing this we will create a revolution in innovation in
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language. In the same way that stable-diffusion helped the world make art and
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images in new ways we hope Open Assistant can help improve the world by
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improving language itself.
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## Do you want to try it out?
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### Contributing to Data Collection
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The data collection frontend is now live [here](https://open-assistant.io/). Log
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in and start taking on tasks! We want to collect a high volume of quality data.
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By submitting, ranking, and labelling model prompts and responses you will be
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directly helping to improve the capabilities of Open Assistant.
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### Running Locally
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**You do not need to run the project locally unless you are contributing to the
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development process. The website link above will take you to the public website
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where you can use the data collection app.**
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If you would like to run the data collection app locally for development, you
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can set up an entire stack needed to run **Open-Assistant**, including the
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website, backend, and associated dependent services, with Docker.
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##### To start the demo, run this in the root directory of the repository:
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```sh
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docker compose up --build
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```
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Then, navigate to `http://localhost:3000` (It may take some time to boot up) and
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interact with the website.
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> **Note:** If an issue occurs with the build, please head to the
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> [FAQ](https://projects.laion.ai/Open-Assistant/docs/faq) and check out the
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> entries about Docker.
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> **Note:** When logging in via email, navigate to `http://localhost:1080` to
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> get the magic email login link.
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> **Note:** If you would like to run this in a standardized development
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> environment (a
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> ["devcontainer"](https://code.visualstudio.com/docs/devcontainers/containers))
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> using
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> [vscode locally](https://code.visualstudio.com/docs/devcontainers/create-dev-container#_create-a-devcontainerjson-file)
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> or in a web browser using
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> [GitHub Codespaces](https://github.com/features/codespaces), you can use the
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> provided [`.devcontainer`](.devcontainer/) folder.
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## The Plan
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##### We want to get to an initial MVP as fast as possible, by following the 3-steps outlined in the [InstructGPT paper](https://arxiv.org/abs/2203.02155).
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1. Collect high-quality human generated Instruction-Fulfillment samples
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(prompt + response), goal >50k. We design a crowdsourced process to collect
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and reviewed prompts. We do not want to train on
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flooding/toxic/spam/junk/personal information data. We will have a
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leaderboard to motivate the community that shows progress and the most active
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users. Swag will be given to the top-contributors.
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2. For each of the collected prompts we will sample multiple completions.
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Completions of one prompt will then be shown randomly to users to rank them
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from best to worst. Again this should happen crowd-sourced, e.g. we need to
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deal with unreliable potentially malicious users. At least multiple votes by
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independent users have to be collected to measure the overall agreement. The
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gathered ranking-data will be used to train a reward model.
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3. Now follows the RLHF training phase based on the prompts and the reward
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model.
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We can then take the resulting model and continue with completion sampling step
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2 for a next iteration.
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## The Vision
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We are not going to stop at replicating ChatGPT. We want to build the assistant
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of the future, able to not only write email and cover letters, but do meaningful
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work, use APIs, dynamically research information, and much more, with the
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ability to be personalized and extended by anyone. And we want to do this in a
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way that is open and accessible, which means we must not only build a great
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assistant, but also make it small and efficient enough to run on consumer
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hardware.
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### Slide Decks
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[Vision & Roadmap](https://docs.google.com/presentation/d/1n7IrAOVOqwdYgiYrXc8Sj0He8krn5MVZO_iLkCjTtu0/edit?usp=sharing)
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[Important Data Structures](https://docs.google.com/presentation/d/1iaX_nxasVWlvPiSNs0cllR9L_1neZq0RJxd6MFEalUY/edit?usp=sharing)
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## How can you help?
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All open source projects begin with people like you. Open source is the belief
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that if we collaborate we can together gift our knowledge and technology to the
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world for the benefit of humanity.
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Check out our [contributing guide](CONTRIBUTING.md) to get started.
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