bebemdjd/LLaMA-3.2-Vision-SFT-for-VQA

LLaMA-3.2-Vision-SFT-for-VQA is a utility script that applies standard supervised fine-tuning (SFT) techniques to a specific multimodal model. With only 1 star and no forks, it currently functions as a personal experiment or a basic reference implementation rather than a community-driven project. It faces extreme competition from established fine-tuning frameworks like Unsloth (which optimizes memory/speed), Axolotl (which provides a unified YAML-based config for dozens of models), and Hugging Face's own TRL library. Furthermore, frontier labs and platform providers like Meta (via llama-recipes) and Hugging Face (via AutoTrain) provide more robust, maintained, and optimized paths for this exact use case. The defensibility is near zero as the code represents a standard assembly of commodity libraries (Transformers, PEFT, DeepSpeed) applied to a popular base model. It is highly likely to be superseded by updates to more generalized training frameworks within months.