Instructions to use RISys-Lab/ReasonCLIP-B32-READ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RISys-Lab/ReasonCLIP-B32-READ 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-READ") 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-READ") model = AutoModelForZeroShotImageClassification.from_pretrained("RISys-Lab/ReasonCLIP-B32-READ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 562 Bytes
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"_name_or_path": "fesvhtr/clip-r-b32-s1-run0109-853",
"architectures": [
"CLIPModel"
],
"dtype": "bfloat16",
"initializer_factor": 1.0,
"logit_scale_init_value": 2.6592,
"model_type": "clip",
"projection_dim": 512,
"text_config": {
"bos_token_id": 0,
"dropout": 0.0,
"dtype": "bfloat16",
"eos_token_id": 2,
"model_type": "clip_text_model"
},
"torch_dtype": "float32",
"transformers_version": "4.48.3",
"vision_config": {
"dropout": 0.0,
"dtype": "bfloat16",
"model_type": "clip_vision_model"
}
}
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