What is Molmo 2
Molmo 2 is an open-source video analysis model launched by the Allen Institute for AI (Ai2), focused on video understanding, pointing, and tracking. The model is based on the Qwen 3 and Olmo architectures, with powerful video analysis capabilities that can precisely locate events in videos, track multiple targets, and generate detailed captions. Molmo 2 surpasses multiple open-source and closed-source models (such asGemini 3) on video tracking and question-answering tasks, while also providing efficient inference capabilities. The model's training dataset is rich, covering a variety of video and image tasks, supporting research and educational use, and helping to advance multimodal intelligence.
Main Features of Molmo 2
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Video Understanding and Question Answering: It can deeply understand video content and answer various questions related to videos, including descriptive questions, event reasoning questions, and more.
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Video Pointing and Localization: It supports precise spatial and temporal localization of specific events or objects in videos, for example answering "when and where a certain event occurred."
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Multi-Object Tracking: It tracks multiple targets in videos and maintains stable tracking when targets are occluded or re-enter the frame.
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Dense Video Captioning: It generates detailed and descriptive captions for videos, providing searchable narrative content for long videos.
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Anomaly and Artifact Detection: It detects anomalous events in videos or artifacts in generated videos, such as inconsistent lighting or errors in object geometry.
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Multi-Image Input Support: It supports a single image, multiple images, and video clips of different lengths as input, providing flexible multimodal processing capabilities.
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Cross-Modal Reasoning: It combines visual and language information for reasoning and supports complex multimodal tasks, such as image or video queries based on text descriptions.
Technical Principles of Molmo 2
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Model Architecture: Molmo 2's architecture consists of a visual encoder, a language model (LLM), and a connector. The visual encoder converts input images or video frames into visual tokens and extracts spatial and temporal information; the language model is based on Qwen 3 or Olmo and processes visual tokens and text information to achieve cross-modal reasoning; the connector interleaves visual tokens with timestamps, image indices, and text, enabling the model to jointly process spatial, temporal, and linguistic information.
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Two-Stage Training: Molmo 2 adopts a two-stage training method. In the first stage, it is pretrained through image captioning and image pointing tasks to enhance the alignment and localization capabilities of the visual and language modalities; in the second stage, it undergoes supervised fine-tuning on multimodal datasets covering image, multi-image, video, and text-only tasks to further improve the model's generalization ability.
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Data Processing and Sampling: For video input, Molmo 2 samples up to 128 frames at a low frame rate (≤2fps) and encodes them through a Vision Transformer. Visual tokens are pooled by temporal windows (such as 3×3), interleaved with text and temporal information, and then fed into the language model, supporting visual token interaction across frames.
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Optimization Techniques: Molmo 2 adopts a token weighting scheme during the fine-tuning stage to balance learning across different tasks and improve the model's performance in multi-task scenarios. At the same time, it introduces sequence packing and message tree scheduling to improve throughput, and enhances localization and tracking capabilities through bidirectional attention mechanisms between visual tokens.
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Dataset and Task Design: Molmo 2 has built a multimodal dataset of over 9 million samples, covering tasks such as dense captioning, video question answering, grounding, and tracking. The training data includes a multi-task mixture of image captioning, video QA, pointing, tracking, and more, enhancing the model's adaptability in complex scenarios.
Molmo 2 Project Address
- Project Website：https://allenai.org/blog/molmo2
- GitHub Repository：https://github.com/allenai/molmo2
- HuggingFace Model Library：https://huggingface.co/collections/allenai/molmo2
- Technical Paper：https://www.datocms-assets.com/64837/1765901660-molmo_v2_2026-techreport-3.pdf
Molmo 2 Application Scenarios
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Autonomous Driving and Traffic Monitoring: Molmo 2 can be used in traffic flow analysis, accident detection, and early warning, improving the safety and efficiency of traffic management and vehicle driving.
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Robotics and Automation: In robot visual navigation and industrial automation inspection, it helps robots better understand the environment and detect product quality issues.
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Scientific Research and Education: It assists in scientific experiment analysis and educational tool development, providing researchers and students with detailed analysis and understanding of dynamic processes.
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Content Creation and Media: It automatically generates video subtitles and assists with video editing, improving the efficiency and accessibility of content creation.
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Security and Surveillance: It detects abnormal behavior and tracks people in security surveillance in real time, ensuring the safety of public places and specific areas.