Instructions to use xiaomoguhzz/VisionEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaomoguhzz/VisionEncoder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xiaomoguhzz/VisionEncoder", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "patcher": "siglip2_to_vjepa21", | |
| "mode": "image", | |
| "from": "siglip2 (SigLIP2 base, num_image_tokens=729, video pool 14\u00b2)", | |
| "to": "vjepa21 (V-JEPA 2.1 ViT-L, num_image_tokens=576, video tubelet skip-pool)", | |
| "delta_formula": "image: lengths[0] += sum(per-image stock _get_number_of_features delta) (num_image_tokens 729 \u2192 576, via LlavaOnevisionProcessor method rebind to stub; captures unpadded + newline + base \u4e09\u9879 \u03b4)", | |
| "input": "/share/m2v_intern_v3/wangjunjie09/VisionEncoder/data/vmllm_cached/siglip2/image_10pct/train", | |
| "rows": 73859, | |
| "lengths_sample_mean_old": 3245.27, | |
| "lengths_sample_mean_new": 2624.35, | |
| "timestamp": "2026-05-11T16:18:06" | |
| } |