How I optimized Andrej Karpathy's "autoresearch" for 2GB GPUs

I’ve been obsessed with Andrej Karpathy’s autoresearch, the idea is that you give an AI agent a training setup and let it experiment with architectures and hyper-params autonomously overnight. The catch? It’s pretty heavy on VRAM. If you’re like me and don't have an H100 in your bedroom, you probably hit an "Out of Memory" error immediately. I spent the last few days building Litesearch to fix that. It’s a fork optimized for consumer hardware (tested everything from an old and dusty GTX 970 (I had to reinstall the drivers) to an RTX 3060). What I added to make it "Lite": Auto VRAM Scaling (probably the best thing so you dont have to manage everything) : It detects your GPU and automatically shrinks or grows the model to fit. No more manual math to avoid crashes. Dark Mode GUI: I used customtkinter to build a dashboard so you can actually watch the VRAM bar and training logs in real-time. The "Try" Button: Once the agent finds a good model, you can chat with it directly in a popup windo