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
Commit History
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training code: train/TRAINING.md
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training code: train/vitals.py
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training code: train/losses.py
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training code: train/dist_bed.py
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training code: train/dist_align_gate.py
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training code: train/cc12m_gpa.py
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training code: train/cc12m_data.py
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training code: train/bank_utils.py
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