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fine-tuning-expert

Fine-tuning LLMs, training custom models

fine-tuning-expert is an agent skill from the jeffallan claude-skills collection that helps a coding agent fine-tune large language models and adapt foundation models to specific tasks. It covers configuring parameter-efficient adapters such as LoRA and QLoRA, preparing data, training and evaluating, and deploying the result. Its workflow has checkpoints at each stage. Dataset preparation validates and formats the data, and the workflow requires the dataset validation script to report no errors before training begins. For method selection, LoRA is preferred for most tasks, QLoRA when GPU memory is limited, and full fine-tuning only for small models. During training the agent configures hyperparameters and watches the loss curves, and validation loss must fall, with a plateau or rise treated as a sign of overfitting. Evaluation benchmarks the tuned model against the base model on a held-out set and edge cases, collecting perplexity, task metrics such as BLEU or ROUGE, and latency. Deployment merges the adapter weights, quantises the model and measures inference throughput before serving. The skill is useful for ML engineers adapting open models. It is listed on skills.sh, where the page shows install counts and GitHub stars for the collection. Its Gen Agent Trust Hub audit is marked as failing, which the FAQ lists, and Socket shows a warning. Install it with npx skills add https://github.com/jeffallan/claude-skills --skill fine-tuning-expert.