Instructions to use zchuning/pi05-siglip-vision-tower with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zchuning/pi05-siglip-vision-tower with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="zchuning/pi05-siglip-vision-tower")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("zchuning/pi05-siglip-vision-tower") model = AutoModel.from_pretrained("zchuning/pi05-siglip-vision-tower", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from zchuning/pi05-siglip-vision-tower: direct link, hf CLI and curl.
- Browser
- Download file 516 Bytes
-
https://huggingface.co/zchuning/pi05-siglip-vision-tower/resolve/main/config.json
- Command line
-
hf download hf://zchuning/pi05-siglip-vision-tower/config.json
-
curl -L -o config.json https://huggingface.co/zchuning/pi05-siglip-vision-tower/resolve/main/config.json
516 Bytes
| { | |
| "architectures": [ | |
| "SiglipVisionModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "dtype": "float32", | |
| "hidden_act": "gelu_pytorch_tanh", | |
| "hidden_size": 1152, | |
| "image_size": 224, | |
| "intermediate_size": 4304, | |
| "layer_norm_eps": 1e-06, | |
| "model_type": "siglip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 27, | |
| "num_image_tokens": 256, | |
| "num_positions": 256, | |
| "patch_size": 14, | |
| "projection_dim": 2304, | |
| "transformers_version": "4.56.2", | |
| "vision_use_head": true | |
| } | |