Instructions to use bn22/CLIPlike-ViT-B-32-Qwen3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bn22/CLIPlike-ViT-B-32-Qwen3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bn22/CLIPlike-ViT-B-32-Qwen3")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("bn22/CLIPlike-ViT-B-32-Qwen3") model = AutoModel.from_pretrained("bn22/CLIPlike-ViT-B-32-Qwen3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 099fa503a26e28614b3fc0fcd37f6b4a7035ee6859501a38b61f107db32d9ddb
- Size of remote file:
- 1.55 GB
- SHA256:
- 8f94eb2c744c9123fba600c0267a810c311de175e7ed08ac80a02888db07ff7e
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