GitHub - 2U1/Qwen-VL-Series-Finetune: An open-source implementaion for fine-tuning Qwen-VL series by Alibaba Cloud. (original) (raw)

This repository contains a script for training Qwen2-VL, Qwen2.5-VL, Qwen3-VL and Qwen3.5 with only using HuggingFace and Liger-Kernel.

Other projects

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[Llama3.2-Vision Finetuning]
[Molmo Finetune]
[Pixtral Finetune]
[SmolVLM Finetune]
[Gemma3 Finetune]

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Table of Contents

Warning

Read Training Notes before running any training script. It contains required settings and compatibility notes for Qwen3.5, QLoRA + vision, QLoRA + liger, DeepSpeed, and video training.

Supported Features

Docker

To simplfy the setting process for training, you could use the provided pre-build environments.
The settings are done in the conda env named train.

You could find more information about the image here.

docker pull john119/vlm
docker run --gpus all -it -v /host/path:/docker/path --name vlm --ipc=host john119/vlm /bin/bash

Installation

Environments

Install the required packages using environment.yaml.

Using requirements.txt

pip install -r requirements.txt -f https://download.pytorch.org/whl/cu128 pip install qwen-vl-utils pip install flash-attn --no-build-isolation

Using environment.yaml

conda env create -f environment.yaml conda activate train pip install qwen-vl-utils pip install flash-attn --no-build-isolation

Note: You should install flash-attn after installing the other packages.

Training Notes

Dataset Preparation

The script requires a dataset formatted according to the LLaVA specification. The dataset should be a JSON file where each entry contains information about conversations and images. Ensure that the image paths in the dataset match the provided --image_folder.

When using a multi-image dataset, the image tokens should all be <image>, and the image file names should have been in a list.

Please see the example below and follow format your data.

Example for single image dataset

[ { "id": "000000033471", "image": "000000033471.jpg", "conversations": [ { "from": "human", "value": "\nWhat are the colors of the bus in the image?" }, { "from": "gpt", "value": "The bus in the image is white and red." }, { "from": "human", "value": "What feature can be seen on the back of the bus?" }, { "from": "gpt", "value": "The back of the bus features an advertisement." }, { "from": "human", "value": "Is the bus driving down the street or pulled off to the side?" }, { "from": "gpt", "value": "The bus is driving down the street, which is crowded with people and other vehicles." } ] } ... ]

Example for multi image dataset

[ { "id": "000000033471", "image": ["000000033471.jpg", "000000033472.jpg"], "conversations": [ { "from": "human", "value": "\n\nIs the perspective of the camera differnt?" }, { "from": "gpt", "value": "Yes, It the perspective of the camera is different." } ] } ... ]

Example for video dataset

[ { "id": "sample1", "video": "sample1.mp4", "conversations": [ { "from": "human", "value": "

Example for DPO dataet

[ { "id": "e4cd017f-3854-4019-9cc1-9dacacb5d424", "image": "example_0_0.png", "prompt": "\nDescribe the key features of the image in great detail.", "chosen": "The image features a modern, multi-colored train parked on a train track, occupying a significant portion of the scene. The train has a unique and striking design, with red, white, and blue colors, as well as yellow accents. It appears to be a high-speed train, indicating its advanced engineering and potential for fast travel.\n\nThe train is situated next to a platform, suggesting that it may be at a train station. The overall scene conveys a sense of transportation and modern technology, with the vibrant and sleek train as the main subject.", "rejected": "The image features a modern, colorful train parked on a set of train tracks. The train has a striking design, with red, white, and blue colors as well as yellow accents. It appears to be a high-speed train, ready for departure.\n\nThe train spans a significant portion of the image, from the left to the right side. The train tracks can be seen clearly beneath the train, emphasizing its position on the railway line. The overall scene gives off an impression of a contemporary and efficient mode of transportation." }, { "id": "5e19e647-e5d3-4bcf-82e9-d262570743ae", "image": "example_1_0.png", "prompt": "\nIs this bus in the USA?", "chosen": "Yes, based on the image, it can be assumed that this bus is in the USA. The location of the bus cannot be accurately determined.", "rejected": "No, it's not in the USA. The image does not provide specific information on where the bus is located. However, we can say that it's not in the United States." } ... ]

Example for GRPO dataset

[ { "id": "06bc8a17-bb1c-4007-8c08-92c41e2628b2", "image": "image_2.jpg", "conversations": [ { "from": "human", "value": "\nBased on the image, which geometric method is used to determine the bearing angle, and why is it the most appropriate choice?" }, { "from": "gpt", "reasoning": "Let's analyze the image step-by-step. The image shows a right-angled triangle with points B, C, and A. The angle at point B is a right angle, indicating that trigonometric functions can be applied. To find the bearing angle, we need to relate the sides of the triangle. The tangent function is suitable here because it relates the opposite side (BC) to the adjacent side (AB) in a right-angled triangle. By using the tangent function, we can calculate the angle at point A, which is the bearing angle. Therefore, the most appropriate geometric method is the use of trigonometric functions.", "value": "A" } ] } ... ]

Reasoning Format

You can keep using the normal dataset format, but if you want to train with an explicit reasoning trace you should add a separate reasoning field instead of manually concatenating <think>...</think> into value.

Use --enable_reasoning True only for the following model families:

When --enable_reasoning True is enabled, the dataset pipeline follows the official chat template behavior for supported models:

This is intended to make training-time formatting match the model's default inference-time chat template as closely as possible for supported reasoning models.

For unsupported models such as Qwen2-VL, Qwen2.5-VL, and non-thinking Qwen3-VL-Instruct, --enable_reasoning True raises an error on purpose.

Qwen3.5 special case

Qwen3-VL-Thinking restriction

SFT / GRPO format

Add reasoning to the assistant turn:

[ { "id": "sample_reasoning", "image": "example.jpg", "conversations": [ { "from": "human", "value": "\nDescribe what happened here." }, { "from": "gpt", "reasoning": "The vehicle is in a place where it normally should not be. It is partially submerged and visibly damaged, so an accident is the most likely explanation.", "value": "A damaged vehicle is partially submerged in a swimming pool." } ] } ]

DPO format

Add chosen_reasoning and rejected_reasoning alongside the corresponding answers:

[ { "id": "sample_dpo_reasoning", "image": "example.jpg", "prompt": "\nDescribe what happened here.", "chosen_reasoning": "The scene is unusual because the vehicle is in a pool and appears damaged, which strongly suggests an accident or deliberate crash scenario.", "chosen": "A damaged vehicle is partially submerged in a swimming pool.", "rejected_reasoning": "The image simply shows a vehicle near water, so there is not enough evidence to say anything unusual happened.", "rejected": "A car is parked beside a swimming pool." } ]

Notes

Adding the new domain-specific data on top of the general data from open-source data will enhance downstream capabilities while retaining the foundational skills. Of course, you can also choose to fine-tune solely on the new data based on your requirements.

Supervised Fine Tuning

⚠️

For dense Qwen3-VL models, full fine-tuning with liger-kernel can be slow. Consider turning off liger-kernel or switching to zero2 in that case.
✅Qwen3-VL-MoE and Qwen3.5-MoE use Liger 0.8.0's fused LigerExperts kernel automatically, so leaving --use_liger_kernel True is recommended for MoE variants.

Tip: You could use adamw_bnb_8bit for optimizer to save memory.

To run the training script, use the following command:

Full Finetuning

Finetune with LoRA

If you want to train only the language model with LoRA and perform full training for the vision model:

bash scripts/finetune_lora.sh

If you want to train both the language model and the vision model with LoRA:

bash scripts/finetune_lora_vision.sh

IMPORTANT: If you want to tune the embed_token with LoRA, You need to tune lm_head together.

Training arguments

Train with video dataset

You can train the model using a video dataset. You can set LoRA configs and use for LoRA too.

bash scripts/finetune_video.sh

When training with video, it behaves like multi-image input, so adjust max_pixels and fps based on the available VRAM.

If you run out of vram, you can use zero3_offload instead of zero3.
You could use zero2_offload for a bit faster training.

Image Resolution for vram usage

The model supprots a wide range of resolution inputs. By default, it uses the native resolution for input. For better performance using native or higer pixel numbers are recommended, however it takes too much memory and computation time for large images. So you could adjust the pixel numbers for it. The model splits the image into token * 28 * 28 so you could just change the the token_num part in the script.

⚠️

For Qwen3-VL models, it should be token * 32 * 32.

For example:

--image_min_pixels $((256 * 28 * 28))
--image_max_pixels $((1280 * 28 * 28))
--video_min_pixels $((128 * 28 * 28))
--video_max_pixels $((768 * 28 * 28))

Besides you could directly set the image/video height and width to control over the memory.

--image_resized_width 448
--image_resized_height 448
--video_resized_width 448
--video_resized_height 448

These values will be rounded to the nearest multiple of 28.

Merge LoRA Weights

bash scripts/merge_lora.sh

Note: Remember to replace the paths in finetune.sh or finetune_lora.sh with your specific paths. (Also in merge_lora.sh when using LoRA.)

Evaluation during Training

You can run generation-based evaluation during training by providing an evaluation dataset and a custom compute_metrics function. This allows you to monitor metrics like accuracy, BLEU, or any custom metric based on the model's generated text outputs.

Step 1: Prepare Evaluation Dataset

The evaluation dataset uses the same format as the training dataset. Place your evaluation data JSON file and specify the path using --eval_path.

[ { "id": "eval_001", "image": "test_image.jpg", "conversations": [ { "from": "human", "value": "\nWhat is shown in this image?" }, { "from": "gpt", "value": "A cat sitting on a couch." } ] } ]

Step 2: Define compute_metrics Function

Create a custom compute_metrics function in your training script. The function receives a GenerativeEvalPrediction object containing:

from src.trainer import GenerativeEvalPrediction

def compute_metrics(eval_pred: GenerativeEvalPrediction): predictions = eval_pred.predictions references = eval_pred.references

# Example: Exact match accuracy
correct = sum(
    1 for p, r in zip(predictions, references)
    if p.strip().lower() == r.strip().lower()
)
accuracy = correct / len(predictions) if predictions else 0

return {"accuracy": accuracy}

Step 3: Modify Training Script

Update your training script (src/train/train_sft.py) to pass compute_metrics to the trainer:

from src.trainer import QwenSFTTrainer, GenerativeEvalPrediction

def compute_metrics(eval_pred: GenerativeEvalPrediction): predictions = eval_pred.predictions references = eval_pred.references correct = sum(1 for p, r in zip(predictions, references) if p.strip() == r.strip()) return {"accuracy": correct / len(predictions)}

... (model and data setup code)

trainer = QwenSFTTrainer( model=model, processing_class=processor, args=training_args, compute_metrics=compute_metrics, # Add this line **data_module )

Step 4: Add Evaluation Arguments

Add these arguments to your training script:

--eval_path /path/to/eval.json
--eval_strategy steps
--eval_steps 500
--per_device_eval_batch_size 1
--generation_max_new_tokens 256
--prediction_loss_only False \

... other arguments

Evaluation Arguments

from src.trainer import GenerativeEvalPrediction import re

def compute_metrics(eval_pred: GenerativeEvalPrediction): predictions = eval_pred.predictions references = eval_pred.references

# Exact match
exact_matches = sum(
    1 for p, r in zip(predictions, references)
    if p.strip().lower() == r.strip().lower()
)

# Contains match (reference appears in prediction)
contains_matches = sum(
    1 for p, r in zip(predictions, references)
    if r.strip().lower() in p.strip().lower()
)

n = len(predictions)
return {
    "exact_match": exact_matches / n if n > 0 else 0,
    "contains_match": contains_matches / n if n > 0 else 0,
}

Note: Generation-based evaluation is slower than loss-only evaluation because it runs model.generate() for each sample. Consider using a smaller evaluation dataset or less frequent evaluation steps.

DPO Finetuning

You can train the model using Direct Preference Optimization (DPO).
The process is quite similar to Supervised Fine-Tuning (SFT), and you can also apply LoRA during DPO training just like in SFT.

If you are training a supported reasoning model, add --enable_reasoning True and provide chosen_reasoning / rejected_reasoning in the dataset as described in Reasoning Format. Each DPO sample must contain both reasoning fields or neither of them, and reasoning-optional samples are supported only for Qwen3.5.

bash scripts/finetune_dpo.sh

Most of the training arugments are same as SFT, but few other arguments are added for DPO training.

Training arguments

GRPO Finetuning

You can traing the model using Group Relative Policy Optimization (GRPO)
The process is quite similar to Supervised Fine-Tuning (SFT), and you can also apply LoRA during GRPO training just like in SFT.

If you are training a supported reasoning model, add --enable_reasoning True and store the assistant reasoning in the reasoning field of the assistant turn as described in Reasoning Format. Reasoning-optional samples are supported only for Qwen3.5.

Prerequisites

What Where Notes
Reward functions src/train/reward_funcs.py Add any function that ends with _reward. The training script picks them up automatically.
Custom system prompts src/constants.py Append your own prompt strings here.

You could start training using this script.
Before training, Please check the dataset format once more. The format is a bit different from other training methods.

bash scripts/finetune_grpo.sh

Most of the training arugments are same as SFT, but few other arguments are added for GRPO training.

Training arguments

Classification Finetuning

The model is tailored for classification tasks, such as other SequenceClassification models.

For the classification task, you need to prepare the dataset in a specific format. The dataset should be a JSON file where each entry contains an image and its corresponding label. The labels should be integers starting from 0.
You can set the text in the filed prompt to provide a questions and options for the classification task. Also if your dataset dose not contain the prompt field, the script will automatically use the USER_MESSAGE from the cls_dataset.py.

Please see the example below for the dataset format.

Example for Classification Dataset

[ { "id": "06bc8a17-bb1c-4007-8c08-92c41e2628b2", "image": "image_2.jpg", "prompt": "Question: What is in the image? \n Options: \n 1. A train \n 2. A bus \n 3. A car \n 4. A bicycle", "label": "3", } ... ]

Note: You should set the CLASS_2_ID variable in the cls_dataset.py.

The dataset can contain single/multi-image or video data, and the model will be trained to classify the images/videos based on the provided labels.

For now, you can select loss from one of the following:

Also you can set early stopping patience and threshold for the training. For example, you can set --early_stopping_patience 5 and --early_stopping_threshold 0.01 to stop the training if the validation loss does not improve for 5 epochs with a threshold of 0.01.

Most of the training arugments are same as SFT, but few other arguments are added for classification training.

Tip: In models like the Qwen family, which have strong context embeddings, even a shallow nonlinearity (a 1-layer MLP) can often improve separability in the tail. This works by introducing a bit of curvature that a purely linear head cannot provide.

Training arguments

You can run the training script using the following command:

bash scripts/finetune_cls.sh

Experimental Features

Inference

Note: You should use the merged weight when trained with LoRA.

Gradio Infernce (WebUI)

  1. Install gradio
  2. Launch app
python -m src.serve.app \
    --model-path /path/to/merged/weight

You can launch gradio based demo with this command. This can also set some other generation configs like repetition_penalty, temperature etc.

Issue for libcudnn error

Could not load library libcudnn_cnn_train.so.8. Error: /usr/local/cuda-12.1/lib/libcudnn_cnn_train.so.8: undefined symbol: _ZN5cudnn3cnn34layerNormFwd_execute_internal_implERKNS_7backend11VariantPackEP11CUstream_stRNS0_18LayerNormFwdParamsERKNS1_20NormForwardOperationEmb, version libcudnn_cnn_infer.so.8

You could run unset LD_LIBRARY_PATH for this error. You could see this issue

TODO

Known Issues

License

This project is licensed under the Apache-2.0 License. See the LICENSE file for details.

Citation

If you find this repository useful in your project, please consider giving a ⭐ and citing:

@misc{Qwen2-VL-Finetuning, author = {Yuwon Lee}, title = {Qwen2-VL-Finetune}, year = {2024}, publisher = {GitHub}, url = {https://github.com/2U1/Qwen2-VL-Finetune} }

Acknowledgement

This project is based on