Miquel Farre developed an end-to-end video-language model fine-tuning pipeline for the huggingface/trl repository, enabling researchers to download videos, process time-coded subtitles, and prepare datasets for Qwen2-VL training. He implemented configurable training workflows with quantization and LoRA, supporting efficient and reproducible experimentation using Python and PyTorch. In the liguodongiot/transformers repository, Miquel improved the robustness of the Idefics3 processor by enhancing error handling for text-only input scenarios, ensuring clear error reporting and reducing downstream failures. His work demonstrated depth in computer vision, data processing, and unit testing, delivering maintainable solutions that address both usability and reliability in model development.