Feature Extraction
Transformers
Safetensors
prism
video
representation-learning
view-invariant
cross-view
egocentric
egoexo4d
emnlp2026
custom_code
Instructions to use litcoderr/prism with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use litcoderr/prism with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="litcoderr/prism", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("litcoderr/prism", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 661 Bytes
a596b0a | 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": [
"PRISMModel"
],
"auto_map": {
"AutoConfig": "configuration_prism.PRISMConfig",
"AutoModel": "modeling_prism.PRISMModel"
},
"d_z": 512,
"ema_decay": 0.998,
"infonce_all_gather": true,
"lambda_decomp": 1.0,
"lambda_temp": 0.5,
"logit_scale_init": 2.6592,
"max_frames": 128,
"mlp_ratio": 4.0,
"model_type": "prism",
"num_heads": 8,
"predictor_depth": 4,
"qformer_depth": 4,
"sliding_shift_aug": true,
"temporal_depth": 12,
"text_backbone_name": "Qwen/Qwen3-Embedding-0.6B",
"transformers_version": "4.57.6",
"use_ema": true,
"vision_backbone_name": "google/siglip2-so400m-patch14-384"
}
|