Hugging Face author trains six models in two days with ML-intern, spending about $103 in total
The model that didn't exist, so you made it yourself
What happened
The author used ML-intern in HuggingChat, describing the requirements in prompts, to build six models in two days and publish them to the Hub, at a total compute cost of about $103. One project distilled the 9B prompt rewriter for Qwen-Image 2.1 into a 0.8B version that runs on a CPU, with a 99.7% valid-output rate and about one-quarter of the teacher model's token usage. The author published all seven prompts and recommends asking for baseline comparisons, smoke tests, and a budget cap.
Why it matters
The author built six models in two days with ML-intern and published all prompts and cost details, offering an approach others can adapt to their own training workflows.
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