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`. | |