ChatGPT
AI conversational assistant for answering questions, writing, and coding help
OpenAssistant
Open-source chat assistant built through collaborative human feedback
A completed open-source project that created a chat-based large language model assistant through crowdsourced data collection and reinforcement learning from human feedback (RLHF).
Overview
Overview
Open Assistant was a community-driven initiative to democratize access to advanced conversational AI by building a chat-based large language model through open collaboration. The project has now concluded, with its final dataset (oasst2) publicly available on HuggingFace.
Approach
The project followed a three-phase methodology inspired by InstructGPT: collecting high-quality human-generated instruction-response pairs through crowdsourcing, ranking multiple completions to train a reward model, and applying reinforcement learning from human feedback (RLHF). Contributors participated through dedicated web interfaces for data collection, prompt submission, response ranking, and quality labeling.
Legacy
Built with Python and TypeScript, Open Assistant aimed to create more than a ChatGPT alternative—the vision encompassed an extensible assistant capable of API integration, dynamic research, and personalization. While the project is complete, it demonstrated how collaborative open-source efforts can produce valuable AI resources. The resulting dataset and learnings continue to benefit the broader AI community, embodying the belief that shared knowledge advances innovation in natural language processing.
Highlights
Crowdsourced data collection with quality controls and contributor leaderboards
Multi-phase RLHF training pipeline following InstructGPT methodology
Complete development stack with Docker-based local setup for contributors
Published oasst2 dataset available on HuggingFace for community use
Pros
- Transparent, community-driven approach to building conversational AI
- Comprehensive dataset (oasst2) freely available for research and development
- Well-documented development process with Docker-based contributor workflow
- Apache-2.0 license enabling broad reuse and adaptation
Considerations
- Project is completed and no longer actively maintained or developed
- Local inference setup requires technical expertise and is not user-friendly
- Crowdsourced data quality dependent on contributor reliability and moderation
- Model performance may lag behind current commercial alternatives
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Fit guide
Great for
- Researchers studying open approaches to RLHF and instruction-tuning
- Developers seeking quality conversational AI training datasets
- Teams building on existing open-source LLM foundations
- Academic projects exploring collaborative AI development methodologies
Not ideal when
- Organizations requiring actively maintained production-ready chat solutions
- Users seeking plug-and-play local chatbot installations
- Projects needing cutting-edge performance matching latest commercial models
- Teams without capacity to adapt or extend completed codebases
How teams use it
Training Data for Custom Assistants
Leverage the oasst2 dataset to fine-tune or evaluate custom conversational models with human-ranked responses
RLHF Research and Experimentation
Study and replicate the three-phase InstructGPT-inspired methodology for academic or commercial research
Open-Source AI Benchmarking
Use the dataset and model artifacts as baselines for comparing new conversational AI approaches
Educational AI Development
Learn collaborative AI development practices by exploring the codebase, documentation, and contribution patterns
Tech snapshot
Python71%
TypeScript27%
JavaScript1%
Shell1%
Mako1%
HTML1%
Frequently asked questions
Is Open Assistant still actively developed?
No, the project is completed and no longer under active development. The team has published the final oasst2 dataset on HuggingFace for community use.
Can I use Open Assistant as a local chatbot?
The local setup is designed for development contributions, not end-user chatbot deployment. Running inference locally requires technical expertise and is not intended as a consumer-ready solution.
Where can I access the dataset?
The final oasst2 dataset is publicly available on HuggingFace at OpenAssistant/oasst2 under the Apache-2.0 license.
What was the data collection methodology?
The project used crowdsourced prompt submission, response generation, and multi-user ranking with quality controls to build training data, following a three-phase RLHF approach inspired by InstructGPT.
Can I contribute to the project?
Since the project is completed, new contributions are not being accepted. However, the codebase and dataset remain available for forking, adaptation, and research under the Apache-2.0 license.
Project at a glance
Dormant- Stars
- 37,470
- Watchers
- 37,470
- Forks
- 3,300
LicenseApache-2.0
Repo age3 years old
Last commitlast year
Primary languagePython
Last synced 50 minutes ago