Hugging Face: an agent trained six small models for a few dollars each

Two Hugging Face staff, Yuvraj Sharma and Abubakar Abid, used HuggingChat's "ML-intern" agent to train and publish six small models in a few days, with evaluations in each model card.

• Prompt rewriter: a 0.8 billion parameter model replaces the official 9 billion parameter rewriter for Qwen-Image 2.1, which takes about 20 GB. It returns valid output 99.7% of the time, uses about a quarter of the original's tokens, and cost $16.
• Citrus-disease vision model: accuracy rose from 14.9% to 52.8% after two passes over the data on a single A10G GPU, for about $1.90.

The prompts used are published on GitHub. The benchmarks are the authors' own.

Why it matters

These are real cost-per-model figures for letting an agent do the fine-tuning. Custom small models are becoming a cheap task you hand off. At that price, nobody will stop to ask whether a person should have done it, which makes checking the result the part that counts.