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)# 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
| { | |
| "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" | |
| } | |