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.