OpenMOSS-Team/MOSS-VL-Realtime-NF4

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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

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:

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.

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.

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

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

@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}
}