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Start here: Try the demo · Quick start · Real-time inference · Offline inference · Model weights

https://github.com/user-attachments/assets/678ec713-0e01-4792-a5b3-c72e483c4d5f

MOSS-VL

MOSS-VL is an open-weight model series for long-form, real-time video understanding, built on a unified cross-attention architecture. All three models are 11B-parameter and open-weight.

• MOSS-VL-Realtime: real-time interaction over continuous video streams — interruptible at any moment, answering on the fly, and deciding on its own when to respond and when to keep watching.
• MOSS-VL-Instruct: built for offline use, with particular strength in complex long-video understanding and in-depth dialogue.
• MOSS-VL-Base: an open pre-trained foundation offering strong video–language representations for continued pre-training and downstream fine-tuning.

Unlike the default paradigm of offline video models ("watch first, answer after"), MOSS-VL-Realtime is designed for real-time interaction on continuous video streams: it runs multimodal perception and text generation in parallel on a continuously arriving stream, natively supporting multi-turn real-time dialogue and dynamic scene understanding, autonomously deciding when to speak, achieving fine-grained temporal grounding, and streaming its responses.

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Key Enhancements

• Interruptible & Real-time: Breaking the limits of offline processing, users can ask questions at any timestamp in the video stream. The model instantly responds based on the currently received frames, achieving open-source SOTA latency in streaming interaction.
• Proactive Silence: Equipped with dynamic judgment capabilities, the model autonomously stays silent and continues observing when contextual information is insufficient or no key events have occurred.
• Dynamic Correction: Cognition updates dynamically with the visual feed. As new frames continue to stream in, the model can instantly capture state changes and correct its previous outputs.

Core Architecture

At the architectural level, MOSS-VL-Realtime adopts the following core designs:

• Cross-Attention Architecture: Decouples visual encoding from language reasoning, significantly reducing response latency on dynamic video streams, with native support for interleaved image, video, and text inputs.
• Absolute Timestamps: Every sampled frame is anchored to a precise time marker via dedicated special tokens. The model uses this to reason about pacing, duration, and motion dynamics, and it natively adapts to variable frame rates.
• XRoPE (Cross-Attention Rotary Position Embedding): Maps text tokens and video patches into a unified 3D (t, h, w) coordinate space, enabling patch-level and moment-level grounding across the entire video.

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🔥 News

• 2026/08/31: ⚖️ Published the MOSS-VL quantization tutorial (English | 中文): our FP8-Dynamic and NF4 recipes, KV-cache quantization, and how to quantize your own fine-tuned (e.g. SFT) MOSS-VL checkpoints.
• 2026/08/28: 📋 Released the list of open-source datasets used in MOSS-VL training.
• 2026/08/21: 🤝 MOSS-VL is now supported as a first-class multimodal model in ms-swift, enabling image/video inference with swift infer and LoRA or full-parameter fine-tuning with swift sft. See PR #9944.
• 2026/08/15: 📚 Published the MOSS-VL Technical Report on arXiv, covering the model architecture, training curriculum, real-time inference system, and comprehensive offline and streaming evaluations.
• 2026/08/14: 🤝 MOSS-VL is now supported by LlamaFactory, with LoRA and full fine-tuning workflows available out of the box. See the English tutorial or Chinese tutorial, and the Mosi AI blog post.
• 2026/08/11: ⚡ Released 24 GiB quantized checkpoints for MOSS-VL, with FP8 and NF4 variants for both Instruct-0708 and Realtime: MOSS-VL-Instruct-0708-FP8 (Hugging Face | ModelScope), MOSS-VL-Instruct-0708-NF4 (Hugging Face | ModelScope), MOSS-VL-Realtime-FP8 (Hugging Face | ModelScope) and MOSS-VL-Realtime-NF4 (Hugging Face | ModelScope), enabling efficient inference on a single 24 GB NVIDIA GPU.
• 2026/07/14: 🏆 MOSS-VL-Realtime achieved 66.0 on PA@OmniMMI and received an official shout-out from OmniMMI.
• 2026/07/14: 🚀 Released MOSS-VL-Realtime for real-time video understanding on continuous streams, together with the new MOSS-VL-Instruct-0708 and MOSS-VL-Base-0708.
• 2026/04/24: 🚀 SGLang officially supports MOSS-VL; see sgl-project/sglang.
• 2026/04/22: 🚀 Released SGLang-based inference support for MOSS-VL. See ./sglang/.
• 2026/04/22: 🤗 Updated HuggingFace inference code to the latest version.
• 2026/04/08: 🚀 Released MOSS-VL-Base-0408 and MOSS-VL-Instruct-0408.

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📊 Performance

MOSS-VL-Realtime delivers significantly stronger streaming interaction capabilities, achieving open-source SOTA results on multiple streaming video understanding benchmarks. Its "proactive speaking" ability stands out in particular: the model leads on all three proactivity evaluations — Proactive Alerting in OmniMMI, Proactive Output in StreamingBench, and ProactiveVideoQA.

Streaming Benchmark

We have systematically restructured and deeply optimized our data system, comprehensively strengthening the model's foundational capabilities and instruction-interaction experience, while maintaining a high level of stability across offline evaluations.

Offline Multimodal Benchmark — Click to Expand

For comprehensive benchmark breakdowns, comparison systems, and detailed tables of all objective metrics, please refer to our Technical Blog.

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🚀 Quick Start

Environment Setup

conda create -n moss_vl python=3.12 pip -y
conda activate moss_vl
pip install -i https://pypi.org/simple --no-build-isolation -r requirements.txt

Real-time Inference

Real-time inference consumes timestamped frames incrementally, so the model can keep perceiving a live video stream while it answers and can accept new questions at any time. The fastest way to replay a local video against its media clock is:

CUDA_VISIBLE_DEVICES=0 python realtime_inference/run_online_inference.py \
  --checkpoint OpenMOSS-Team/MOSS-VL-Realtime \
  --source video \
  --video path/to/example.mp4 \
  --sample-fps 1 \
  --playback-speed 1 \
  --max-frames 256

Keep --playback-speed 1 for model inference so frames arrive on the original timeline. The runtime provides three integration levels:

• model.create_realtime_session(...) for direct frame, prompt, and output control
• model.online_generate(...) for queue-based inference workers
• --serve for a FastAPI WebSocket service that accepts external JPEG/PNG frames or replays server-local videos

It also supports streaming JSONL samples, cameras, screen capture, and synthetic sources. See realtime_inference/README.md for the complete CLI, input format, and WebSocket protocol.

Offline Inference

Offline inference supports full-modality queries (interleaved text, image, and video inputs). The fastest way to get a first result is offline_batch_generate:

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

checkpoint = "OpenMOSS-Team/MOSS-VL-Realtime"

processor = AutoProcessor.from_pretrained(checkpoint, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    checkpoint, trust_remote_code=True, device_map="auto", torch_dtype=torch.bfloat16
)

queries = [{
    "messages": [{"role": "user", "content": [
        {"type": "image", "image": "path/to/example.jpg"},
        {"type": "text", "text": "Describe this image."}
    ]}],
    "generate_kwargs": {"max_new_tokens": 256, "do_sample": False},
}]

with torch.no_grad():
    result = model.offline_batch_generate(processor, queries)

print([item["text"] for item in result["results"]])

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🛠️ Advanced Resources & Ecosystem

Specialized FlashAttention-3 Backend

The flash-attention-src/ directory contains the FlashAttention-3 backend used by MOSS-VL cross-attention. It adds the cross_kv_boundary interface, which represents the visible KV prefix of each query row with one int32 value instead of materializing a dense attention mask. The source is derived from upstream FlashAttention and is bundled here with its original license and attribution. See flash-attention-src/README.md for the mask contract, supported paths, build instructions, and source lineage.

Deployment & Inference Engines

MOSS-VL can also be efficiently deployed with the following inference backends:

• SGLang: see sglang/README.md

Fine-Tuning

We provide a lightweight SFT framework built on HuggingFace transformers.Trainer. It supports full-parameter training and LoRA, with the vision encoder, language model, and LM head independently controllable.

# Full-parameter SFT (vision encoder frozen by default)
bash finetune/scripts/run_sft.sh

# LoRA SFT
pip install -i https://pypi.org/simple peft
bash finetune/scripts/run_sft_lora.sh

See finetune/README.md for full documentation.

Quantization

We release FP8 and NF4 quantized checkpoints for both Instruct-0708 and Realtime, and share the calibration-free PTQ recipes behind them in quant/README.md (中文教程). The guide covers selective layer coverage — which language-model Linears to quantize versus which multimodal modules stay in BF16 — runtime KV-cache quantization for Transformers and SGLang, and reproduction scripts that work directly on your own fine-tuned or SFT checkpoints.

Model Download

This generation ships three models from the same rebuilt data: MOSS-VL-Realtime for continuous video streams, Instruct for offline tasks, and Base for continued pre-training and fine-tuning.

Previous generation:

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📑 Roadmap & TODO List

✅ Milestones

• Core Architecture: Implementation of Cross-attention RoPE (XRoPE).
• High-performance Infra: Integrated Megatron-LM + CUDA Flash Attention 3.
• Model Release: Open-sourced MOSS-VL-Base and MOSS-VL-Instruct.
• Inference: Inference code for both image and video understanding.
• Real-time Capabilities: Released MOSS-VL-Realtime — real-time video understanding on continuous streams.

🚀 Upcoming

• Training Engine: Full training code for MOSS-VL.
• RL Post-training: Reinforcement Learning for MOSS-VL series.
• Documentation: Published the MOSS-VL Technical Report.
• Cookbooks: Task-level runnable notebooks.

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🤝 Acknowledgement

We would like to express our gratitude to NVIDIA for the Megatron-LM framework and the Qwen Team for their powerful Qwen series language models, which serve as the foundation of our training infrastructure and core LLM. We also thank the SGLang Team for their high-performance SGLang serving framework, which powers efficient deployment of MOSS-VL.

📜 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}
}

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