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
File size: 780 Bytes
60edb88 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"created_at": "2026-08-11T04:18:07.460954+00:00",
"source_model": "/inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/final_release/MOSS-VL-0708-Instruct-sglang",
"output_dir": "/inspire/qb-ilm/project/video-understanding/public/train/moss_vl_streaming/8B/final_release/quant/MOSS-VL-0708-Instruct-SGLang-NF4-KV8",
"runtime": "native SGLang MOSS-VL",
"weight_format": "bitsandbytes NF4 W4A16 keep-first-last-4",
"quantized_linear_count": 240,
"kv_cache": "native SGLang FP8 E4M3 via runtime argument",
"tp_support": "prequantized bitsandbytes is TP1 only in current SGLang",
"peak_gpu_memory_gib_during_conversion": 11.038,
"versions": {
"torch": "2.8.0+cu128",
"transformers": "4.57.1",
"bitsandbytes": "0.49.2"
}
}
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