The Tiny Labs Platform: Infrastructure for Open AI Research

Tag: General · Dec 12, 2024

Lowering Barriers to AI Research

At Tiny Labs, we believe that the best ideas can come from anywhere. Our platform is designed to remove the technical and institutional frictions that hold back community contributors, providing a transparent pipeline from idea to evaluation to recognition. Anyone with a good idea can test it, improve it with peers, and get credit for their impact.

Key Features

• Experiment Hub: Track proposals, results, and progress in one place.
• Managed Job Launcher: Run training and evaluation jobs without worrying about infrastructure.
• Contributor Dashboards: See your impact, stats, and recognition at a glance.
• Community Auction System: Bi-weekly/monthly voting to prioritize the most promising experiments.
• Automated Payouts: Contributors are rewarded based on measurable impact.
• Sponsor Engagement: Sponsors can directly fund and interact with top contributors.

The Research Pipeline

Contributors propose experiments via GitHub issues—ranging from architectural modifications and optimizer changes to data variations and training strategies. These proposals can be implemented either through configuration changes or direct code contributions as pull requests. On a regular cycle, the community and sponsors vote on the most promising ideas through an open auction system. Selected experiments are evaluated against general benchmarks like MMLU, as well as domain-specific tests, optionally proposed by sponsors. The top-performing contributions are merged into the main model, ensuring continuous, community-led progress.

Monetization & The Future

Tiny Labs plans to monetize by partnering with sponsors and private companies to develop domain-specific small language models tailored to their needs. Leveraging the open research conducted by our community, we provide custom fine-tuning, benchmarking, and deployment support on top of our core models. This allows organizations to benefit from cutting-edge, efficient language models optimized for their specific use cases—while directly supporting the open research ecosystem that powers them.

Scaling Open, Reproducible Research

Our vision is to create a platform where open, collaborative research is not just possible, but the default. By making it easy to propose, test, and share new ideas, we hope to accelerate progress in AI and make the field more inclusive, transparent, and impactful for everyone.