Visual Question Answering
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
multimodal
text-generation-inference
Instructions to use TIGER-Lab/VL-Reasoner-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TIGER-Lab/VL-Reasoner-72B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="TIGER-Lab/VL-Reasoner-72B")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("TIGER-Lab/VL-Reasoner-72B") model = AutoModelForImageTextToText.from_pretrained("TIGER-Lab/VL-Reasoner-72B") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: visual-question-answering
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# VL-Reasoner-72B
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**VL-Reasoner-72B** achieves superior results on various multimodal reasoning benchmarks.
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It is trained using the **GRPO-SSR** techniques, serving as the foundation for [**VL-Rethinker**](https://huggingface.co/TIGER-Lab/VL-Rethinker-72B/).
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pipeline_tag: visual-question-answering
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# VL-Reasoner-72B
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**🚀 News:** <u>We release our meticulously curated collection of RL training queries for multimodal reasoning: [ViRL39K](https://huggingface.co/datasets/TIGER-Lab/ViRL39K).</u>
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**VL-Reasoner-72B** achieves superior results on various multimodal reasoning benchmarks.
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It is trained using the **GRPO-SSR** techniques, serving as the foundation for [**VL-Rethinker**](https://huggingface.co/TIGER-Lab/VL-Rethinker-72B/).
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