elvis@omarsar0 · X作者探索利用Fireworks AI Agent，通过自然语言交互自动化完成大语言模型的微调流程。他以Qwen小模型为例，调整其输出风格以优化PaperWiki项目的扩展效率。这一方法灵感源于@karpathy关于LLM知识库的推文，强调微调是让模型更“懂”数据的关键步骤。核心观点是自动化微调可推动构建可递归自我改进的AI系统，最终目标是打造一个能自我优化、用于知识发现和端到端自动化研究的强大工具。
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.