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
File size: 780 Bytes
fcca0aa | 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 | {
"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
}
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