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
| { | |
| "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" | |
| } | |