OpenMOSS-Team / MOSS-VL-Instruct-0408

MOSS-VL-Instruct-0408

📌 Introduction

MOSS-VL-Instruct-0408 is the instruction-tuned checkpoint of the MOSS-VL series, part of the OpenMOSS ecosystem dedicated to advancing visual understanding.

Built on top of MOSS-VL-Base-0408 through supervised fine-tuning (SFT), this checkpoint is designed as a high-performance offline multimodal engine. It delivers strong, well-rounded performance across the full spectrum of vision-language tasks — including image understanding, OCR, document parsing, visual reasoning, and instruction following — and is particularly outstanding at video understanding, from long-form comprehension to fine-grained temporal reasoning and action recognition.

✨ Highlights

• 🎬 Outstanding Video Understanding — A core strength of MOSS-VL. The model excels at long-form video comprehension, temporal reasoning, action recognition, and second-level event localization, delivering top-tier results on benchmarks such as VideoMME, and MLVU.
• 🖼️ Strong General Multimodal Perception — Robust image understanding, fine-grained object recognition, OCR, and document parsing.
• 💬 Reliable Instruction Following — Substantially improved alignment with user intent through supervised fine-tuning on diverse multimodal instruction data.

🏗 Model Architecture

MOSS-VL-Instruct-0408 adopts a cross-attention-based architecture that decouples visual encoding from cognitive reasoning. This design drives latency down to the millisecond level , enabling instantaneous responses to dynamic video streams. Natively supporting interleaved modalities , it processes complex sequences of images and videos within a unified pipeline — eliminating the need for heavy pre-processing.

🧩 Absolute Timestamps

To ensure the model accurately perceives the pacing and duration of events, MOSS-VL-Instruct-0408 injects absolute timestamps alongside each sampled frame, grounding the reasoning process in a precise temporal reference .

🧬 Cross-attention RoPE (XRoPE)

MOSS-VL utilizes Cross-attention Rotary Position Embedding (XRoPE), tailored to its cross-attention based vision–language architecture. This mechanism maps text tokens and video patches into a unified 3D coordinate space defined by Time (t), Height (h), and Width (w).

📊 Model Performance

We conducted a comprehensive evaluation of MOSS-VL-Instruct-0408 across four key dimensions: Multimodal Perception, Multimodal Reasoning, Document/OCR, and Video Understanding. The results demonstrate that MOSS-VL achieves outstanding performance, particularly excelling in general multimodal perception and complex video analysis .

🌟 Key Highlights

• 🚀 Leading Video Intelligence : MOSS-VL achieves a score of 65.8 in Video Understanding, significantly outperforming Qwen3-VL (+2pts). It shows exceptional temporal consistency and action recognition capabilities across benchmarks like VideoMME , MLVU , EgoSchema , and VSI-bench (where it outperforms Qwen3-VL-8B-Instruct by 8.3 points ).
• 👁️ Outstanding Multimodal Perception : MOSS-VL delivers excellent general image-text understanding, shining in fine-grained object recognition and spatial reasoning on benchmarks like BLINK and MMBench .
• 🧠 Robust Multimodal Reasoning : MOSS-VL demonstrates solid logical inference, staying highly competitive with the latest Qwen series on challenging reasoning suites.
• 📄 Reliable Document Understanding : While the model is primarily optimized for general perception, MOSS-VL still delivers 83.9 on OCR and document analysis, ensuring dependable extraction of text and structured information.

🚀 Quickstart

🛠️ Installation

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

🏃 Run Inference

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

checkpoint = "path/to/checkpoint"
image_path = "data/example_image.jpg"
prompt = "Describe this image."


def load_model(checkpoint: str):
    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",
    )
    return model, processor


model, processor = load_model(checkpoint)

text = model.offline_image_generate(
    processor,
    prompt=prompt,
    image=image_path,
    shortest_edge=4096,
    longest_edge=16777216,
    multi_image_max_pixels=201326592,
    patch_size=16,
    temporal_patch_size=1,
    merge_size=2,
    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)

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

checkpoint = "path/to/checkpoint"
video_path = "data/example_video.mp4"
prompt = "Describe this video."


def load_model(checkpoint: str):
    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",
    )
    return model, processor


model, processor = load_model(checkpoint)

text = model.offline_video_generate(
    processor,
    prompt=prompt,
    video=video_path,
    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)

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

checkpoint = "path/to/checkpoint"
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",
)

queries = [
    {
        "prompt": "Describe sample A.",
        "images": [],
        "videos": ["data/sample_a.mp4"],
        "media_kwargs": {"video_fps": 1.0, "min_frames": 8, "max_frames": 256},
        "generate_kwargs": {
            "temperature": 1.0,
            "top_k": 50,
            "top_p": 1.0,
            "max_new_tokens": 256,
            "repetition_penalty": 1.0,
            "do_sample": False,
        },
    },
    {
        "prompt": "Describe sample B.",
        "images": [],
        "videos": ["data/sample_b.mp4"],
        "media_kwargs": {"video_fps": 1.0, "min_frames": 8, "max_frames": 256},
        "generate_kwargs": {
            "temperature": 1.0,
            "top_k": 50,
            "top_p": 1.0,
            "max_new_tokens": 256,
            "repetition_penalty": 1.0,
            "do_sample": False,
        },
    },
]

with torch.no_grad():
    result = model.offline_batch_generate(processor, queries, vision_chunked_length=64)

texts = [item["text"] for item in result["results"]]

🚧 Limitations and Future Work

MOSS-VL-Instruct-0408 represents an early milestone in the MOSS-VL roadmap, and we're actively working on several directions to push it further:

• 🧮 Math & Code Reasoning — While the current checkpoint already exhibits great general reasoning, we plan to substantially strengthen its mathematical reasoning and code reasoning capabilities, especially in multimodal contexts.
• 🎯 RL Post-Training — We are working on a reinforcement learning post-training stage to further align the model with human preferences and to unlock stronger multi-step reasoning behaviors on top of the SFT foundation.

We welcome community feedback and contributions on any of these directions.

📜 Citation

@misc{moss_vl_2026,
  title         = {{MOSS-VL Technical Report}},
  author        = {OpenMOSS Team},
  year          = {2026},
  howpublished  = {\url{https://github.com/OpenMOSS/MOSS-VL}},
  note          = {GitHub repository}
}