This repository provides a comprehensive, production-grade training framework for fine-tuning the Qwen-VL family of vision-language models (Qwen2-VL, Qwen2.5-VL, Qwen3-VL, Qwen3.5) using HuggingFace Transformers, Liger-Kernel, and DeepSpeed. It supports Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and classification fine-tuning. The architecture is modular: dataset loaders handle mixed-modality (image/video/text) data with reasoning field support, custom trainer subclasses optimize memory and enable generation-based evaluation, and model wrappers provide sequence classification heads with configurable MLP bridges. Key engineering achievements include monkey-patched vision transformer forward passes for reduced memory usage, robust handling of DeepSpeed ZeRO-3 with dummy vision inputs for text-only batches, and a workaround for PEFT/DeepSpeed compatibility. The codebase is actively maintained, dependencies are pinned, and the project demonstrates strong craftsmanship with several reusable components.