Video-Text-to-Text
Transformers
Safetensors
qwen2_5_vl
spatial-reasoning
4d-vision
vlm
text-generation-inference
Instructions to use TencentARC/DSR_Suite-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TencentARC/DSR_Suite-Model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration_Spatial processor = AutoProcessor.from_pretrained("TencentARC/DSR_Suite-Model") model = Qwen2_5_VLForConditionalGeneration_Spatial.from_pretrained("TencentARC/DSR_Suite-Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add model card and metadata for DSR Suite model
Verified signatureHi! I'm Niels from the Hugging Face community science team. I've opened this PR to add a comprehensive model card and metadata for your DSR Suite model. This PR: - Adds the `video-text-to-text` pipeline tag for better discoverability. - Adds the `library_name: transformers` tag as indicated by the `config.json` and `tokenizer_config.json` files, enabling automated code snippets. - Links the model to the paper [Learning to Reason in 4D: Dynamic Spatial Understanding for Vision Language Models](https://huggingface.co/papers/2512.20557). - Includes links to the project's GitHub repository, the associated Hugging Face dataset, and collection. - Provides an introduction summarizing the model's capabilities in dynamic spatial reasoning and details on its usage for evaluation. - Includes the correct BibTeX citation and acknowledgements. Please review and merge if this looks good to you!2b0baa8
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Initial Commit
stewiezhou5de562d
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initial commit
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