- Building a GenAI landscape
- Build GenAI solution stack/reference architecture/ecosystem
- Identify open source projects in the MAD area for LF AI & Data
- Host open discussions and build thought leadership in the MAD area
- Support LF AI & Data GenAI projects/help grow ecosystem if needed
Generative AI, particularly Large Language Models (LLMs), is rapidly becoming key to enterprise and cross-industry applications. LLMs have versatile applications including:
- Text generation
- Natural language understanding
- Conversational dialog, code generation
- Emerging multi-modal capabilities
The LF AI & Data Generative AI Commons is committed to promoting the democratization, advancement, and adoption of efficient, secure, reliable, and ethical Generative AI open source innovations.
Through neutral governance, open and transparent collaboration, and education, the Generative AI Commons fosters open source and open-science principles. This initiative aims to create a neutral and inclusive community where organizations collaborate and contribute to developing enterprise-ready platforms, effectively filling a critical gap in the generative AI landscape.
Not-for-profitA non-profit that hosts and promotes the collaborative development of open source projects (sw, hw, standards) |
NeutralTrusted neutral foundation hosting projects and their IP, encourages organizations to collaborate & contribute |
Open GovernanceOpen, transparent and fair governance model across all efforts and projects |
Credible3000+ members, 900+ projects, leading OSS technologies in all sectors |
Initial Gen AI Commons Workstreams
Models, Applications and Data Workstream
Frameworks Workstream
- Model Openness Framework and Report
- Responsible AI Framework
- Generative AI Reference Architectures
- Develop Best Practices and Guidelines
Education and Outreach Workstream
- Offer Generative AI Training and Certification
- Provide Thought Leadership
- Perform Academic and Member Outreach
- Advocat for Open Source AI with Governments
Responsible AI Workstream
- Responsible AI
- Security, Privacy and Safety
- Informing Policy
- Copyright Issues
- Model and Data Lineage