Self-improving AI is a big deal!
TL;DR · AI Summary
Using FireworksAI Agent to automate LLM fine-tuning demonstrates the feasibility of self-improving AI systems, enabling model iteration through natural language interaction. Future recursive self-improvement systems could revolutionize knowledge discovery and end-to-end research automation.
Key Takeaways
- FireworksAI Agent automates LLM fine-tuning, successfully optimizing Qwen's outp
- Claude Code and natural language instructions form a closed-loop system for mode
- Recursive self-improvement systems could drastically enhance knowledge discovery
Outline
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Introduces initial experiments using FireworksAI Agent to automate LLM fine-tuning
Claude Code and natural language instructions used to fine-tune Qwen via Fireworks Agent
Demonstrates closed-loop system for knowledge base construction and model iteration in PaperWiki project
Proposes building recursive self-improvement systems for advanced AI autonomy
Mindmap
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- Self-improving AI系统
- 自动化后训练
- FireworksAI Agent
- 自然语言指令
- PaperWiki项目
- 知识库构建
- 模型迭代闭环
- 递归自我改进
- 知识发现
- 研究自动化
Highlights
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Here is a first post on how I am using @FireworksAI_HQ Agent to automate LLM fine-tuning itself.
All done via natural language. This is obviously the future of improving AI systems.
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-en
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" / X
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
Agent to automate LLM fine-tuning itself. Dataset + Skill file included. For the use case, I took inspiration from
'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 (x.com/omarsar0/statu). 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.