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)# 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
File size: 487 Bytes
a242999 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | {
"apply_rotation": true,
"architectures": [
"ClipMiniModel"
],
"auto_map": {
"AutoConfig": "configuration_clip_mini.ClipMiniConfig",
"AutoModel": "modeling_clip_mini.ClipMiniModel"
},
"dtype": "float32",
"has_rotation": true,
"hidden_size": 240,
"image_size": 160,
"model_type": "clip_vitb_mini",
"num_attention_heads": 4,
"num_hidden_layers": 12,
"patch_size": 16,
"projection_dim": 512,
"transformers_version": "5.13.0"
}
|