Image Feature Extraction
Transformers
Safetensors
clip_vitb_mini
feature-extraction
clip
knowledge-distillation
consensus-distillation
vit
custom_code
Instructions to use AbstractPhil/clip-vitb-mini-distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbstractPhil/clip-vitb-mini-distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="AbstractPhil/clip-vitb-mini-distilled", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbstractPhil/clip-vitb-mini-distilled", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
gen-1 checkpoint set: coco/infonce_s1_t8000.pt
Browse files- coco/infonce_s1_t8000.pt +3 -0
coco/infonce_s1_t8000.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:667628dafa8101818e0a8463e3d615894ff7bea694ed3a1014f47df32db1e7f9
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size 34710685
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