Text Generation
PEFT
Safetensors
opensportslib
sports
soccer
vqa
video-question-answering
qwen-vl
lora
conversational
Instructions to use OpenSportsLab/OSL-VQA-XFOUL-qwen3-8B-VL-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use OpenSportsLab/OSL-VQA-XFOUL-qwen3-8B-VL-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-8B-Instruct") model = PeftModel.from_pretrained(base_model, "OpenSportsLab/OSL-VQA-XFOUL-qwen3-8B-VL-lora") - Notebooks
- Google Colab
- Kaggle
OpenSportsLib VQA Model (Qwen3-VL LoRA)
Overview
This model is a VQA LoRA adapter produced with OpenSportsLib for soccer foul understanding and referee-style visual question answering.
- Task: Visual Question Answering (VQA)
- Architecture: Qwen3-VL + LoRA adapter
- Backend:
qwen_vl_native_lora - Base model:
Qwen/Qwen3-VL-8B-Instruct - Library: OpenSportsLib
- Input: Soccer video clips plus natural-language questions
- Visual path: Native end-to-end QwenVL video understanding
Dataset
Training Dataset
This adapter was trained on the OpenSportsLib XFoul VQA setup built on the OSL-XFoul dataset.
- Dataset name:
OSL-XFoul - Domain: Soccer video understanding and officiating
- Task: Visual question answering
- Modality: Video + text
- Training samples: 16,568
- Validation samples: 2,219
Benchmark Results
| Accuracy | Balanced Accuracy |
|---|---|
| 69.96% | 48.33% |
Using with OpenSportsLib
For more details about OpenSportsLib:
- GitHub: https://github.com/OpenSportsLab/opensportslib
- PyPI: https://pypi.org/project/opensportslib/
- Documentation: https://opensportslab.github.io/opensportslib/
Run inference
from opensportslib.apis import VQAModel
my_model = VQAModel(
config="opensportslib/configs/vqa/qwen3_vl_native.yaml",
weights="YOUR_HF_REPO_ID",
)
predictions = my_model.infer(
test_set="/path/to/test_annotations.json",
)
single_prediction = my_model.infer(
video_path="/path/to/video.mp4",
question="Was this a foul? What card should be given?",
)
print(predictions)
print(single_prediction)
Notes
- This repository stores a PEFT LoRA adapter, not a merged standalone base model.
- The adapter is intended for the OpenSportsLib native QwenVL VQA path driven
by
opensportslib/configs/vqa/qwen3_vl_native.yaml. - The original training setup used frame-based native multimodal inputs with
Qwen/Qwen3-VL-8B-Instruct.
License
- Open source license: AGPL 3.0 for research, academic, and community use.
- Commercial license: For proprietary or commercial deployment, please contact the project maintainers.
Citation
@misc{opensportslib_qwen3_vl_vqa_xfoul_lora_2026,
title={OpenSportsLib Qwen3-VL LoRA for Soccer VQA},
author={OpenSportsLab},
year={2026},
howpublished={https://huggingface.co/OpenSportsLab}
}
Acknowledgements
- Dataset: OpenSportsLab / OSL-XFoul
- Library: https://github.com/OpenSportsLab/opensportslib
- Model pipeline: OpenSportsLib native QwenVL VQA backend
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Model tree for OpenSportsLab/OSL-VQA-XFOUL-qwen3-8B-VL-lora
Base model
Qwen/Qwen3-VL-8B-Instruct