The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight. It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You wake up in the morning to a log of experiments and (hopefully) a better model. The training code here is a simplified single-GPU implementation of nanochat. The core idea is that you're not touching any of the Python files like you normally would as a researcher. Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org. The default program.md in this repo is intentionally kept as a bare bones baseline, though it's obvious how one would iterate on it over time to find the "research org code" that achieves the fastest research progress, how you'd add more agents to the mix, etc. A bit more context on this project is here in this tweet.
Full Moon - The whole face of the Moon is illuminated and fully visible.
，更多细节参见新收录的资料
但是，大多时候，我们只是想要一个测试的过程和结果而已。也许，这是人类内心无法由理性控制的冲动。起源于公元前6世纪的德尔斐箴言，最有名的一句就是镌刻在阿波罗神殿门前的“认识你自己”。
Елизавета Городищева (Редактор отдела «Экономика»)
pip install git+https://github.com/RichardAtCT/[email protected]