Instructions to use tanganke/clip-vit-base-patch32_cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanganke/clip-vit-base-patch32_cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tanganke/clip-vit-base-patch32_cifar10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("tanganke/clip-vit-base-patch32_cifar10") model = AutoModel.from_pretrained("tanganke/clip-vit-base-patch32_cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from tanganke/clip-vit-base-patch32_cifar10: direct link, hf CLI and curl.
- Browser
- Download file 543 Bytes
-
https://huggingface.co/tanganke/clip-vit-base-patch32_cifar10/resolve/main/config.json
- Command line
-
hf download hf://tanganke/clip-vit-base-patch32_cifar10/config.json
-
curl -L -o config.json https://huggingface.co/tanganke/clip-vit-base-patch32_cifar10/resolve/main/config.json
543 Bytes
| { | |
| "_name_or_path": "results/temp/", | |
| "architectures": [ | |
| "CLIPVisionModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "dropout": 0.0, | |
| "hidden_act": "quick_gelu", | |
| "hidden_size": 768, | |
| "image_size": 224, | |
| "initializer_factor": 1.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 12, | |
| "num_channels": 3, | |
| "num_hidden_layers": 12, | |
| "patch_size": 32, | |
| "projection_dim": 512, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.39.1" | |
| } | |