Video-Text-to-Text
Transformers
Safetensors
English
Chinese
moss_vl
feature-extraction
MOSS-VL
realtime
streaming
video-understanding
FP8
compressed-tensors
HQQ
quantized
custom_code
Instructions to use OpenMOSS-Team/MOSS-VL-Realtime-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-VL-Realtime-FP8 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSS-Team/MOSS-VL-Realtime-FP8", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
OpenMOSS-Team/MOSS-VL-Realtime-FP8
- Runtime: Transformers
online_generate/create_realtime_session. - Weights: compressed-tensors FP8 E4M3 on 252 self-attention/MLP Linear layers.
- Activations: FP8 per-token dynamic on the same 252 Linear inputs.
- BF16: cross-attention layers, vision encoder/merger, embeddings, norms and
lm_head. - KV cache: Transformers HQQ INT8, group size 64, residual length 128.
- Attention backend:
flash_attention_2.