---
title: MOSS-VL-Realtime-NF4
canonical_url: "https://www.modelscope.cn/models/openmoss/MOSS-VL-Realtime-NF4"
md_url: "https://www.modelscope.cn/models/openmoss/MOSS-VL-Realtime-NF4.md"
repository: openmoss/MOSS-VL-Realtime-NF4
chinese_name: MOSS-VL-Realtime-NF4
last_updated: 2026-09-23
license: apache-2.0
model_type:
- moss_vl
architectures:
- MossVLForConditionalGeneration
base_model:
- OpenMOSS-Team/MOSS-VL-Realtime
base_model_relation: quantized
parameters: 11.6B
tensor_type:
- F32
- U8
- BF16
library_name:
- pytorch
- transformer
- safetensors
language:
- en
- zh
downloads: 121
stars: 3
tags:
- MOSS-VL
- realtime
- streaming
- video-understanding
- bitsandbytes
- NF4
- quantized
- custom_code
---
# MOSS-VL-Realtime-NF4
> MOSS-VL-Realtime-NF4 - openmoss 在 ModelScope 开源的模型。MOSS-VL-Realtime W4A16 NF4 + KV8 HQQ
openmoss/MOSS-VL-Realtime-NF4 是 ModelScope 魔搭社区上的 11.6B 参数机器学习模型，采用 apache-2.0 许可，基于 OpenMOSS-Team/MOSS-VL-Realtime 构建。
- **Repository**: openmoss/MOSS-VL-Realtime-NF4
- **License**: apache-2.0
- **Parameters**: 11.6B
- **Base model**: OpenMOSS-Team/MOSS-VL-Realtime
- **Tags**: MOSS-VL, realtime, streaming, video-understanding, bitsandbytes, NF4, quantized, custom_code
- **Downloads**: 121
- **Stars**: 3
- **Last updated**: 2026-09-23
Source: https://www.modelscope.cn/models/openmoss/MOSS-VL-Realtime-NF4
---
English | 中文
# MOSS-VL-Realtime W4A16 NF4 + KV8 HQQ
MOSS-VL is an open vision-language model family from OpenMOSS, supporting image understanding, long-video understanding, and realtime streaming interaction. This repository provides the W4A16 NF4 + KV8-quantized checkpoint of MOSS-VL-Realtime.
**Technical Report**: [https://arxiv.org/pdf/2608.15045](https://arxiv.org/pdf/2608.15045)
This is the 24 GiB quantized release of MOSS-VL-Realtime. It keeps the original
timestamp-aware streaming interface and can also use the offline image/video
helpers from the standard checkpoint.
## Quantization profile
| Component | Format |
| --- | --- |
| Most language layers | bitsandbytes NF4 4-bit weights with double quantization |
| First/last language layers and multimodal modules | BF16 |
| Activations and compute | BF16 |
| Transformers KV cache | HQQ INT8 |
| Attention backend | FlashAttention 2 |
The checkpoint carries its bitsandbytes configuration, HQQ cache configuration,
and MOSS-VL remote modeling code. Load the directory directly; do not add a
second runtime quantization configuration.
## Quantization benchmark
Across the selected benchmarks, the quantized models remain close to their
non-quantized BF16 counterparts, showing that overall model quality is largely
preserved after quantization.
## Hardware requirements
The model is designed to run on a single NVIDIA GPU with 24 GB of VRAM. Use
FlashAttention 2 and `frame_queue_size=1` for the 24 GB realtime profile.
## Environment
### Installation
Use the standard MOSS-VL repository requirements, then add the two quantization
backends required by this checkpoint:
```bash
git clone https://github.com/OpenMOSS/MOSS-VL.git
cd MOSS-VL
conda create -n moss_vl_quant python=3.12 pip -y
conda activate moss_vl_quant
pip install -i https://pypi.org/simple --no-build-isolation -r requirements.txt
pip install -i https://pypi.org/simple \
bitsandbytes==0.49.2 \
hqq==0.2.8.post1
python -m pip check
```
The standard release environment uses the following core stack:
| Package | Version |
| --- | --- |
| Python | 3.12 |
| PyTorch | 2.8.0 + CUDA 12.8 |
| Transformers | 4.57.1 |
| Accelerate | 1.12.0 |
| FlashAttention | 2.8.1 |
| bitsandbytes | 0.49.2 |
| HQQ | 0.2.8.post1 |
Video decoding also requires FFmpeg to be available in `PATH`.
## Load the model
Keep `attn_implementation` set to `flash_attention_2` for the 24 GB profile.
```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
checkpoint = "/path/to/mossvl_streaming_w4a16_nf4_keep_first4_last4_kv8_hqq"
processor = AutoProcessor.from_pretrained(
checkpoint,
trust_remote_code=True,
frame_extract_num_threads=1,
)
model = AutoModelForCausalLM.from_pretrained(
checkpoint,
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
)
model.eval()
```
`generation_config.json` automatically enables the HQQ INT8 KV cache. Do not
override it with a BF16/dynamic cache when using the 24 GB profile.
## Realtime inference
The application supplies PIL-compatible frames with non-decreasing timestamps.
Use `frame_queue_size=1` for the 24 GB realtime profile.
```python
import time
from PIL import Image
session = model.create_realtime_session(
processor,
initial_prompt=(
"Describe important changes in the video as they happen. "
"Stay silent when there is no meaningful update."
),
frame_queue_size=1,
max_tokens_per_turn=12,
max_new_tokens=4096,
do_sample=False,
)
frame_paths = [
"data/frame_0001.jpg",
"data/frame_0002.jpg",
"data/frame_0003.jpg",
]
try:
session.start()
for index, frame_path in enumerate(frame_paths):
image = Image.open(frame_path).convert("RGB")
session.push_frame(image, timestamp=float(index))
while True:
chunk = session.poll_output(timeout=0.0)
if chunk is None:
break
print(chunk, end="", flush=True)
time.sleep(1.0)
session.push_prompt("What changed in the latest frames?")
deadline = time.monotonic() + 5.0
while time.monotonic() < deadline:
chunk = session.poll_output(timeout=0.1)
if chunk is not None:
print(chunk, end="", flush=True)
finally:
session.close()
```
One model instance supports one active realtime session. The model may emit
control tokens such as `<|silence|>`, `<|round_start|>`, and `<|round_end|>`;
applications should filter or render them according to their protocol.
## Offline video inference
```python
text = model.offline_video_generate(
processor,
prompt="Describe this video.",
video="data/example_video.mp4",
shortest_edge=4096,
longest_edge=16777216,
video_max_pixels=201326592,
patch_size=16,
temporal_patch_size=1,
merge_size=2,
video_fps=1.0,
min_frames=1,
max_frames=256,
num_extract_threads=4,
image_mean=[0.5, 0.5, 0.5],
image_std=[0.5, 0.5, 0.5],
max_new_tokens=256,
temperature=1.0,
top_k=50,
top_p=1.0,
repetition_penalty=1.0,
do_sample=False,
vision_chunked_length=64,
)
print(text)
```
## Configuration files
- `config.json`: model and NF4 weight configuration.
- `generation_config.json`: HQQ KV8 configuration.
- `modeling_moss_vl.py`: checkpoint-local MOSS-VL and QuantizedCache code.
## Citation
```bibtex
@misc{mossvl,
title = {MOSS-VL Technical Report},
author = {Wang, Pengyu and Tan, Chenkun and Zhou, Shaojun and Zhou, Qirui and Chen, Yanxin and He, Xingyang and Zeng, Huazheng and Cheng, Jijun and Wang, Chenghao and Qian, Xiaomeng and Wang, Pengfei and Huang, Zhan and Gao, Shanqing and Huang, Wei and Cao, Longjun and Ran, Wu and Liu, Jie and Zhu, Changtai and Wang, Hongkai and Tian, Yixian and Liu, Chenghao and Ye, Zhen and Wang, Xinghao and Jiang, Botian and Feng, Guoguo and Fei, Zhaoye and Li, Ruixiao and Chen, Mingshu and Gao, Yang and Cheng, Qinyuan and Li, Shimin and Qiu, Xipeng},
year = {2026},
eprint = {2608.15045},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2608.15045}
}
@misc{mossvideopreview,
title = {{MOSS-Video-Preview: Toward Real-Time Video Understanding via Cross-Attention}},
author = {Pengyu Wang and Chenkun Tan and Shaojun Zhou and Wei Huang and Qirui Zhou and Zhan Huang and Zhen Ye and Jijun Cheng and Xiaomeng Qian and Yanxin Chen and Xingyang He and Huazheng Zeng and Chenghao Wang and Pengfei Wang and Hongkai Wang and Shanqing Gao and Yixian Tian and Chenghao Liu and Xinghao Wang and Botian Jiang and Xipeng Qiu},
year = {2026},
eprint = {2606.07639},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2606.07639}
}
```