Beam Creates a Gemma-4 Fine-Tuning Notebook via opencode
UnslothAI said it connected Beam to opencode and asked it to create a fine-tuning notebook for the smallest Gemma-4 model on a Text2SQL task, an out-of-distribution domain for Beam. According to UnslothAI, Beam researched model cards, Unsloth documentation and README files, implemented the fine-tuning logic, and raised Gemma’s accuracy by 66.5% on a held-out test set; these performance details were not independently verified.
The vendor-reported demonstration shows an AI coding agent combining documentation research with implementation of a model fine-tuning workflow.
RT Reflection We plugged Beam into @opencode, asked it to look at @UnslothAI, and create a fine-tuning notebook for the latest and smallest Gemma-4 model on a Text2SQL task – an out of distribution domain for Beam. The model successfully researches the model cards, Unsloth documentation, and available READMEs, and implements the fine-tuning logic and surrounding machinery. As a result, Beam is able to successfully finetune Gemma, increasing Gemma’s accuracy on its heldout test set by 66.5%.