Video-Text-to-Text
Transformers
Safetensors
English
Chinese
moss_vl
feature-extraction
MOSS-VL
image-understanding
video-understanding
bitsandbytes
NF4
quantized
custom_code
4-bit precision
Instructions to use OpenMOSS-Team/MOSS-VL-Instruct-0708-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSS-Team/MOSS-VL-Instruct-0708-NF4 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSS-Team/MOSS-VL-Instruct-0708-NF4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # OpenMOSS-Team/MOSS-VL-Instruct-0708-NF4 | |
| - Runtime: Transformers `offline_generate`. | |
| - Weights: bitsandbytes NF4 with double quantization on 240 eligible Linear layers. | |
| - Compute: BF16. | |
| - BF16: first/last four language layers, cross-attention projections, vision encoder/merger, embeddings, norms and `lm_head`. | |
| - KV cache: BF16 (`KV16`); HQQ KV8 is not enabled. | |
| - SGLang compatibility is not claimed for this checkpoint. | |