2U1/Qwen-VL-Series-Finetune

Health v2 · maintenance only

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1960 stars · 222 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An open-source Python repository providing training scripts for fine-tuning Alibaba's Qwen-VL series of vision-language models (Qwen2-VL, Qwen2.5-VL, Qwen3-VL, Qwen3.5) using HuggingFace Transformers and Liger-Kernel. It supports SFT, DPO, GRPO, LoRA/DoRA, classification, and multi-image/video training with memory optimizations.

Use cases

• fine-tune qwen2-vl on custom image datasets
• train qwen3-vl with lora on my own data
• run dpo training on a vision language model
• grpo training for multimodal models
• fine-tune qwen2.5-vl for video understanding
• train a vlm classifier on custom categories
• reduce gpu memory when fine-tuning vision language models

When to choose

• you need to fine-tune any Qwen-VL series model with SFT, DPO, or GRPO
• you want memory-efficient training via Liger-Kernel and attention optimizations
• you need LoRA/DoRA, partial layer freezing, or mixed-modality (image/video) training support

When to avoid

• you want to fine-tune non-Qwen vision-language models (use the author's sibling repos or generic frameworks like LLaMA-Factory)
• you need inference/serving rather than training
• you prefer a GUI or no-code fine-tuning workflow

Facets

library · maturity active

llm-training machine-learning deep-learning machine-learning deep-learning large-language-models computer-vision python fine-tuning vision-language-model qwen-vl multimodal lora dpo grpo liger-kernel huggingface gpu

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markdown · JSON · MCP: product_card(name="2U1/Qwen-VL-Series-Finetune")