Video-Text-to-Text
Transformers
Safetensors
English
Chinese
moss_vl
feature-extraction
MOSS-VL
realtime
streaming
video-understanding
bitsandbytes
NF4
quantized
custom_code
4-bit precision
Instructions to use OpenMOSS-Team/MOSS-VL-Realtime-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-VL-Realtime-NF4 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSS-Team/MOSS-VL-Realtime-NF4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "created_at": "2026-07-26T06:34:21.056464+00:00", | |
| "source_weight_checkpoint": "/inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/quant/mossvl_streaming_w4a16_nf4_keep_first4_last4", | |
| "weight_quantization": "bitsandbytes NF4 W4A16", | |
| "kv_cache_quantization": { | |
| "backend": "hqq", | |
| "nbits": 8, | |
| "axis_key": 0, | |
| "axis_value": 0, | |
| "q_group_size": 64, | |
| "residual_length": 128 | |
| }, | |
| "storage": "Model shards are hard-linked to the source checkpoint when the filesystem supports hard links.", | |
| "compatibility_patch": "Cross-attention materializes the quantized history one layer at a time because the original MOSS-VL code directly reads cache_layer.keys." | |
| } | |