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Researchers and developers interested in a tool for advancing applications in vision-language understanding and interaction
About Molmo
Molmo is a family of open, state-of-the-art multimodal AI models developed by the Allen Institute for AI (Ai2). These models are designed to bridge the gap between open and proprietary systems, achieving competitive performance across a wide range of academic benchmarks and human evaluations. Unlike many existing multimodal models that rely heavily on synthetic data from proprietary systems, Molmo is trained entirely on open data, ensuring transparency and reproducibility. A key innovation in Molmo's development is the introduction of PixMo, a novel dataset comprising highly detailed image captions collected from human annotators using speech-based descriptions, as well as 2D pointing data that enables the models to answer questions using both natural language and non-verbal cues. This allows Molmo to interact with its environment in more nuanced ways, such as pointing to objects within images, thereby enhancing its applicability in fields like robotics and augmented reality.
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Molmo 2
Molmo 2 is a new suite of state-of-the-art open vision-language models with fully open weights, training data, and training code that extends the original Molmo family’s grounded image understanding to video and multi-image inputs, enabling advanced video understanding, pointing, tracking, dense captioning, and question-answering capabilities; all with strong spatial and temporal reasoning across frames. Molmo 2 includes three variants: an 8 billion-parameter model optimized for overall video grounding and QA, a 4 billion-parameter version designed for efficiency, and a 7 billion-parameter Olmo-backed model offering a fully open end-to-end architecture including the underlying language model. These models outperform earlier Molmo versions on core benchmarks and set new open-model high-water marks for image and video understanding tasks, often competing with substantially larger proprietary systems while training on a fraction of the data used by comparable closed models.
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Olmo 2
Olmo 2 is a family of fully open language models developed by the Allen Institute for AI (AI2), designed to provide researchers and developers with transparent access to training data, open-source code, reproducible training recipes, and comprehensive evaluations. These models are trained on up to 5 trillion tokens and are competitive with leading open-weight models like Llama 3.1 on English academic benchmarks. Olmo 2 emphasizes training stability, implementing techniques to prevent loss spikes during long training runs, and utilizes staged training interventions during late pretraining to address capability deficiencies. The models incorporate state-of-the-art post-training methodologies from AI2's Tülu 3, resulting in the creation of Olmo 2-Instruct models. An actionable evaluation framework, the Open Language Modeling Evaluation System (OLMES), was established to guide improvements through development stages, consisting of 20 evaluation benchmarks assessing core capabilities.
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Ai2
Founded: 2014
United States
allenai.org/blog/molmo
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