dbiswas55/vlm-qlora-finetuning

This repository is a compact QLoRA fine-tuning and inference setup for chart question answering with vision-language models. Its documented workflow is smoke test, train, evaluate, then predict; the graph focuses on the training, evaluation, comparison, and prediction runtime. Training formats ChartQA examples, prepares masked image/text batches, loads a quantized model, and saves a LoRA adapter. Evaluation scores adapter predictions; comparison reports base-versus-adapter results. The README documents distributed launch and other capabilities, but their implementation details are not sampled here.