Instructions to use facebook/vjepa2-vitg-fpc64-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/vjepa2-vitg-fpc64-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="facebook/vjepa2-vitg-fpc64-384")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("facebook/vjepa2-vitg-fpc64-384") model = AutoModelForMultimodalLM.from_pretrained("facebook/vjepa2-vitg-fpc64-384") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "VJEPA2Model" | |
| ], | |
| "attention_probs_dropout_prob": 0.0, | |
| "crop_size": 384, | |
| "drop_path_rate": 0.0, | |
| "frames_per_clip": 64, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.0, | |
| "hidden_size": 1408, | |
| "image_size": 384, | |
| "in_chans": 3, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-06, | |
| "mlp_ratio": 4.363636363636363, | |
| "model_type": "vjepa2", | |
| "num_attention_heads": 22, | |
| "num_hidden_layers": 40, | |
| "patch_size": 16, | |
| "pred_hidden_size": 384, | |
| "pred_mlp_ratio": 4.0, | |
| "pred_num_attention_heads": 12, | |
| "pred_num_hidden_layers": 12, | |
| "pred_num_mask_tokens": 10, | |
| "pred_zero_init_mask_tokens": true, | |
| "qkv_bias": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.53.0.dev0", | |
| "tubelet_size": 2, | |
| "wide_SiLU": true | |
| } | |