Visual Question Answering
Transformers
Safetensors
Chinese
English
qwen3_vl
image-text-to-text
multimodal
vision-language
mechanical-drawing
vqa
mechvqa
Instructions to use XiaofengAlg/MechVL-4B-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XiaofengAlg/MechVL-4B-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="XiaofengAlg/MechVL-4B-SFT")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("XiaofengAlg/MechVL-4B-SFT") model = AutoModelForMultimodalLM.from_pretrained("XiaofengAlg/MechVL-4B-SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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license: apache-2.0
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language:
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tags:
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base_model: Qwen/Qwen3-VL-4B-Instruct
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pipeline_tag: visual-question-answering
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library_name: transformers
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# MechVL-4B-SFT
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## License
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Apache-2.0.
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license: apache-2.0
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language:
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- en
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tags:
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- multimodal
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- vision-language
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- mechanical-drawing
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- vqa
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- mechvqa
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base_model: Qwen/Qwen3-VL-4B-Instruct
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pipeline_tag: visual-question-answering
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library_name: transformers
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datasets:
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- XiaofengAlg/MechVQA
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# MechVL-4B-SFT
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## License
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Apache-2.0.
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