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"], )# 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") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: cc-by-nc-sa-4.0 | |
| tags: [] | |
| ## Model Details | |
| - Model: ReasonCLIP-B32-S1 | |
| - Base model: [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) | |
| - Architecture: CLIP ViT-B/32 | |
| - Image resolution: 224 | |
| - Training stage: Stage 1 | |
| - Training data: [ReasonLite-42M](https://huggingface.co/datasets/RISys-Lab/ReasonCLIPLite-42M) | |
| ## Method | |
|  | |
| ## Resources | |
| - GitHub: [RISys-Lab/ReasonCLIP](https://github.com/RISys-Lab/ReasonCLIP) | |
| - Paper: [arXiv:2606.26794](https://arxiv.org/abs/2606.26794) | |
| ## Usage | |
| ```python | |
| from transformers import CLIPModel, CLIPProcessor | |
| model_id = "RISys-Lab/ReasonCLIP-B32-S1" | |
| model = CLIPModel.from_pretrained(model_id) | |
| processor = CLIPProcessor.from_pretrained(model_id) | |
| ``` | |
| For the full checkpoint list, see the [ReasonCLIP model card](https://github.com/RISys-Lab/ReasonCLIP/blob/main/doc/model_card.md). | |