@omarsar0: Self-improving AI is a big deal! As a first step, I've been exploring how much of the post-training can be automated. H…
Summary
Marco Oram shares his exploration of automating LLM fine-tuning using Fireworks AI Agent, fine-tuning a small Qwen model to integrate with his PaperWiki project, demonstrating a step toward self-improving AI.
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Self-improving AI is a big deal!
As a first step, I’ve been exploring how much of the post-training can be automated.
Here is a first post on how I am using @FireworksAI_HQ Agent to automate LLM fine-tuning itself.
Dataset + Skill file included.
For the use case, I took inspiration from @karpathy’s tweet on LLM Knowledge Bases.
I asked Claude Code to interact with Fireworks Agent to fine-tune a small Qwen model to get the right output style to efficiently keep growing my PaperWiki (https://x.com/omarsar0/status/2042286186920550498?s=20…).
All done via natural language. This is obviously the future of improving AI systems.
The next step with the PaperWiki project is how to tune a model to better “know” the data. Harder to do, but if possible, then we have an incredibly powerful system that can recursively self-improve and can be extremely useful for things like knowledge discovery and automating all kinds of research end-to-end.
More on this soon. Thanks to the Fireworks team for allowing me to test this early. Super excited about this.
@omarsar0 I achieved that recently lol
Post is attached with a link to the doc.
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