Image Feature Extraction
Transformers
Safetensors
mage_vit
feature-extraction
mage-vl
vision-encoder
codec-vit
video-understanding
custom_code
Instructions to use microsoft/Mage-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Mage-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="microsoft/Mage-ViT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/Mage-ViT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 649 Bytes
28ba2c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"architectures": [
"MageViTModel"
],
"attention_dropout": 0.0,
"dtype": "bfloat16",
"hidden_act": "gelu",
"hidden_size": 1024,
"image_size": 256,
"initializer_range": 0.02,
"intermediate_size": 4096,
"layer_norm_eps": 1e-06,
"layer_norm_type": "layer_norm",
"model_type": "mage_vit",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 24,
"patch_size": 16,
"rope_theta": 10000.0,
"rope_temporal_size": 64,
"transformers_version": "5.7.0",
"use_head": true,
"auto_map": {
"AutoConfig": "configuration_mage_vit.MageViTConfig",
"AutoModel": "modeling_mage_vit.MageViTModel"
}
}
|