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An open-source implementaion for fine-tuning Qwen-VL series by Alibaba Cloud.
| Date | Stars |
|---|---|
| 2026-07-24 | 1942 |
| 2026-07-25 | 1942 |
| 2026-07-28 | 1943 |
| 2026-07-30 | 1947 |
| 2026-08-06 | 1947 |
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# Fine-tuning Qwen-VL Series
This repository contains a script for training [Qwen2-VL](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), [Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
, [Qwen3-VL](https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking) and [Qwen3.5](https://huggingface.co/Qwen/Qwen3.5-4B) with only using HuggingFace and [Liger-Kernel](https://github.com/linkedin/Liger-Kernel).
## Other projects
**[[Phi3-Vision Finetuning]](https://github.com/2U1/Phi3-Vision-Finetune)**<br>
**[[Llama3.2-Vision Finetuning]](https://github.com/2U1/Llama3.2-Vision-Ft)**<br>
**[[Molmo Finetune]](https://github.com/2U1/Molmo-Finetune)**<br>
**[[Pixtral Finetune]](https://github.com/2U1/Pixtral-Finetune)**<br>
**[[SmolVLM Finetune]](https://github.com/2U1/SmolVLM-Finetune)**<br>
**[[Gemma3 Finetune]](https://github.com/2U1/Gemma3-Finetune)**
## Update
- [2026/05/18] 🔥**Upgrade to `liger_kernel==0.8.0`.** Liger 0.8.0 adds official patches for `qwen3_5` / `qwen3_5_moe` and ships **LigerExperts**, a fused MoE expert kernel that auto-accelerates `qwen3_vl_moe` and `qwen3_5_moe` under `--use_liger_kernel True`. The 0.7-era hardcoded fallback that force-disabled Liger for Qwen3.5 in SFT/DPO/GRPO has been removed, and the `mm_token_type_ids` GRPO wrapper is now skipped automatically on Liger ≥ 0.8.0 (kept as a no-op shim for older installs).
- [2026/03/07] 🔥**Supports reasoning mode training for Qwen3-VL and Qwen3.5**
- [2026/03/07] 🔥**Supports Qwen3.5 Series.**
- [2026/03/07] Supports Qwen3-VL classification
- [2026/03/07] Update codebase to `transformers==5.3.0`
- [2025/11/28] 🔥**Supports video training with DPO and GRPO.**
- [2025/11/27] 🔥**Supports Qwen3-VL-MoE**
- [2025/11/26] Update support for liger-kernel in Qwen3-VL.
- [2025/10/16] 🔥**Supports Qwen3-VL(non-moe)**
- [2025/08/21] Add option for using 2-layer mlp for classification.
- [2025/08/21] Add option for unfreeze only few layers for llm and vision tower.
- [2025/08/08] 🔥Monkey patch Qwen2.5-VL's window attention and forward for using less memory and speedups.
- [2025/07/25] Updated Classification training script.
- [2025/05/29] 🔥Supports GRPO training.
- [2025/04/16] 🔥Supports DPO training.
- [2025/03/04] Add Option for using liger kernel.
- [2025/02/18] 🔥Supports mixed-modality dataset with zero3.
- [2025/02/05] Fixed code for properly use image.
- [2025/02/03] Support Liger-kernel for Qwen2.5-VL.
- [2025/02/03] 🔥Supports Qwen2.5-VL.
- [2025/01/24] Add option for using DoRA.
- [2025/01/24] Fix error in LoRA training.
- [2025/01/18] 🔥Supports mixed-modality data.
- [2024/09/12] 🔥Now the model is trained using [Liger-Kernel](https://github.com/linkedin/Liger-Kernel).
- [2024/09/11] Supports setting different learning rates to projector and vision model.
- [2024/09/11] 🔥Supports multi-image and video training.
## Table of Contents
- [Fine-tuning Qwen-VL Series](#fine-tuning-qwen-vl-series)
- [Other projects](#other-projects)
- [Update](#update)
- [Table of Contents](#table-of-contents)
- [Supported Features](#supported-features)
- [Docker](#docker)
- [Installation](#installation)
- [Environments](#environments)
- [Using `requirements.txt`](#using-requirementstxt)
- [Using `environment.yaml`](#using-environmentyaml)
- [Training Notes](#training-notes)
- [Dataset Preparation](#dataset-preparation)
- [Reasoning Format](#reasoning-format)
- [Supervised Fine Tuning](#supervised-fine-tuning)
- [Full Finetuning](#full-finetuning)
- [Finetune with LoRA](#finetune-with-lora)
- [Train with video dataset](#train-with-video-dataset)
- [Image Resolution for vram usage](#image-resolution-for-vram-usage)
- [Merge LoRA Weights](#merge-lora-weights)
- [Evaluation during Training](#evaluation-during-training)
- [Step 1: Prepare Evaluation Dataset](#step-1-prepare-evaluation-dataset)
- [Step 2: Define compute\_metrics Function](#step-2-define-compute_metrics-function)
- [Step 3: Modify Training ScriExcerpt of 36,169 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e3c27c00e19516d4, topic:multimodal, topic:vision-language-model, topic:vlm
matched fp:e3c27c00e19516d4, desc:fine-tuning, readme:fine-tuning, desc:fine tuning