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
| # MOSS-VL quantized KV-cache variant | |
| - Weights: bitsandbytes NF4 W4A16 | |
| - KV cache: HQQ 8-bit | |
| - Quantization group size: 64 | |
| - BF16 residual window: 128 tokens per layer | |
| - Vision/cross-attention weights remain BF16. | |
| - Requires the quant venv with the selected KV backend installed. | |