🚀 Revolutionizing AI Training with Tinkerer
I created Tinkerer, a groundbreaking system that empowers frontier AI agents (like Claude Code and OpenAI Codex) to autonomously fine-tune language models without human intervention. After conducting over 100 experiments, I discovered:
- Impressive Results: The best setup achieved near-perfect arithmetic in just 18 minutes for under $1.
- Key Findings: While these agents can execute training pipelines effectively, they lack the judgment necessary for advanced ML research.
- Innovative Approach: The “Prompt-to-Train” concept allows anyone, regardless of ML experience, to define model objectives while AI handles the rest.
🔍 Insights from the Journey
- Execution vs. Research: Training is procedural; research involves discernment.
- Learning Gaps: Agents performed well but sometimes missed critical issues, like a malfunctioning learning rate scheduler.
Tinkerer represents a significant leap toward autonomous AI research. It’s open-source!
👉 Explore the code and run experiments at GitHub. Share your thoughts and let’s reshape the future of AI together!
