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Cataloged from OpenMOSS-Team/MOSS-VL-Instruct-0408
All models by OpenMOSS-Team โ[Image blocked: No description]
MOSS-VL is an open vision-language model family from OpenMOSS, supporting image understanding, long-video understanding, and realtime streaming interaction.
Technical Report: https://arxiv.org/pdf/2608.15045
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 โ including image understanding, OCR, document parsing, visual reasoning, and instruction following โ and is particularly outstanding at video understanding, from long-form comprehension to fine-grained temporal reasoning and action recognition.
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 โ eliminating the need for heavy pre-processing.
[Image blocked: MOSS-VL Architecture]
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.
[Image blocked: Timestamped Sequence Input Illustration]
MOSS-VL utilizes Cross-attention Rotary Position Embedding (XRoPE), tailored to its cross-attention based visionโlanguage architecture. This mechanism maps text tokens and video patches into a unified 3D coordinate space defined by Time (t), Height (h), and Width (w).
[Image blocked: MOSS-VL mRoPE Architecture Illustration]
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.
VideoMME, MLVU, EgoSchema, and VSI-bench (where it outperforms Qwen3-VL-8B-Instruct by 8.3 points).BLINK and MMBench.[Image blocked: MOSS-VL Benchmark Results]
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:
[!NOTE] We welcome community feedback and contributions on any of these directions.
conda create -n moss_vl python=3.12 pip -yconda activate moss_vlpip install -i https://pypi.org/simple --no-build-isolation -r requirements.txtimport torchfrom 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 torchfrom 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 torchfrom 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"]]@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}}