LLM-Finetuning-Toolkit

Why we included this project

Fine-tuning an open-source LLM usually means wiring together a training script, a prompting scheme, and a way to judge the output. This toolkit trades most of that setup for one YAML file: you declare the prompts, pick one or more Hugging Face models, set LoRA or QLoRA quantization, and describe the QA checks, and the CLI runs the experiment and files the outputs under a hashed directory. That structure pays off for ablation work, since a single config can take a list of variations and sweep through them in one run instead of juggling separate scripts. It also includes evaluation helpers like length and word-overlap checks, a quick way to confirm a tuned checkpoint still handles something such as summarization sensibly. Because results, model weights, and test output land in predictable folders, a small team can rerun and audit experiments without standing up a heavier orchestration stack.