Instructions to use RISys-Lab/ReasonCLIP-B32-S1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RISys-Lab/ReasonCLIP-B32-S1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="RISys-Lab/ReasonCLIP-B32-S1") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("RISys-Lab/ReasonCLIP-B32-S1") model = AutoModelForZeroShotImageClassification.from_pretrained("RISys-Lab/ReasonCLIP-B32-S1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add model card, paper link, license, and usage example
Verified signatureHi! I'm opening this PR to replace the placeholder model card with a documented model card for ReasonCLIP. Specifically, this PR: - Adds the `pipeline_tag: zero-shot-image-classification` and `license: cc-by-nc-sa-4.0` (Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International) to the metadata. - Links the model card to the paper [ReasonCLIP-58M: Visually Grounded Commonsense Reasoning Supervision for CLIP](https://huggingface.co/papers/2606.26794). - Adds a link to the official GitHub repository (https://github.com/RISys-Lab/ReasonCLIP). - Includes the `transformers` Quick Start code snippet provided in the repository's README. Let me know if you have any questions or feedback!800a7fd
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Upload processor
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Copy weights from fesvhtr/clip-r-b32-s1-run0109-853
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initial commit
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