Feature Extraction
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
embedding
multimodal
vision-language
mmeb
mveb
Instructions to use HugC/VisME-Qwen25VL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HugC/VisME-Qwen25VL-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="HugC/VisME-Qwen25VL-7B")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("HugC/VisME-Qwen25VL-7B") model = AutoModelForMultimodalLM.from_pretrained("HugC/VisME-Qwen25VL-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag to model card metadata
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by nielsr HF Staff - opened
README.md
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license: apache-2.0
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tags:
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- embedding
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- multimodal
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- feature-extraction
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- mmeb
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- mveb
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base_model: Qwen/Qwen2.5-VL-7B-Instruct
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library_name: transformers
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---
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# VisME (Qwen2.5-VL-7B)
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## License
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This model is built upon [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). Please follow the license terms of the base model and the respective datasets used during training.
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---
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base_model: Qwen/Qwen2.5-VL-7B-Instruct
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library_name: transformers
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license: apache-2.0
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pipeline_tag: feature-extraction
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tags:
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- embedding
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- multimodal
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- feature-extraction
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- mmeb
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- mveb
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---
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# VisME (Qwen2.5-VL-7B)
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## License
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This model is built upon [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct). Please follow the license terms of the base model and the respective datasets used during training.
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