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
File size: 993 Bytes
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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).
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