Instructions to use AI4Industry/MoonViT-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Industry/MoonViT-V2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="AI4Industry/MoonViT-V2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AI4Industry/MoonViT-V2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 942 Bytes
35e4140 | 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 29 30 31 32 33 34 35 36 | {
"architectures": [
"MoonViTV2PretrainedModel"
],
"auto_map": {
"AutoConfig": "configuration_moonvit_v2.MoonViTV2Config",
"AutoModel": "modeling_moonvit_v2.MoonViTV2PretrainedModel",
"AutoImageProcessor": "image_processing_moonvit_v2.MoonViTV2ImageProcessor"
},
"model_type": "moonvit_v2",
"patch_size": 14,
"num_attention_heads": 12,
"num_hidden_layers": 27,
"hidden_size": 1024,
"intermediate_size": 4096,
"qkv_hidden_size": 1536,
"init_pos_emb_height": 64,
"init_pos_emb_width": 64,
"init_pos_emb_time": 4,
"pos_emb_type": "divided_fixed",
"pos_emb_interpolation_mode": "bilinear",
"merge_kernel_size": [
2,
2
],
"merge_type": "sd2_tpool",
"norm_type": "rmsnorm",
"mlp_type": "mlp2",
"attn_bias": false,
"linear_bias": false,
"patch_embed_proj_bias": false,
"activation_func": "gelu_pytorch_tanh",
"torch_dtype": "bfloat16",
"transformers_version": "4.50.3"
}
|