Supporting the Open Source AI Community
We believe artificial intelligence has the power to save the world — and that a thriving open source ecosystem is essential to building this future.
Thankfully, the open source ecosystem is starting to develop, and we are now seeing open source models that rival closed-source alternatives. Hundreds of small teams and individuals are also working to make these models more useful, accessible, and performant.
These projects push the state of the art in open source AI and help provide a more robust and comprehensive understanding of the technology. They include: instruction-tuning base LLMs; removing censorship from LLM outputs; optimizing models for low-powered machines; building novel tooling for model inference; researching LLM security issues; and many others.
However, the people behind these projects often don’t have the resources available to pursue their work to conclusion or maintain it in the long run. The situation is more acute in AI than traditional infrastructure, since even fine-tuning models requires significant GPU computing resources, especially as open source models get larger.
Obligatory xkcd homage (original)
To help close this resource gap, we’re announcing today the a16z Open Source AI Grant program. We’ll support a small group of open source developers through grant funding (not an investment or SAFE note), giving them the opportunity to continue their work without the pressure to generate financial returns.
We’re also announcing the first batch of grant recipients and funded projects:
- Jon Durbin (Airoboros): instruction-tuning LLMs on synthetic data
- Eric Hartford: fine-tuning uncensored LLMs
- Jeremy Howard (fast.ai): fine-tuning foundation models for vertical applications
- Tom Jobbins (TheBloke): quantizing LLMs to run locally
- Woosuk Kwon and Zhuohan Li (vLLM): library for high-throughput LLM inference
- Nous Research: new fine-tuned language models akin to the Nous Hermes and Puffin series
- oobabooga: web UI and platform for local LLMs
- Teknium: synthetic data pipelines for LLM training
We want to thank them for their contributions to the field, and for fostering open collaboration, learning, and advancement in AI.
