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
| { | |
| "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" | |
| } | |