HomeAntGroup Unveils Ling-3.0-flash-VL, a 124B-Parameter Native Multimodal Large Model
The Ant Open Source team has officially launched Ling-3.0-flash-VL, the first native multimodal large model in the Bailing series. Built on the MoE architecture of Ling-3.0-flash, this model features 124B total parameters, with only 5.5B activated per inference. It natively supports image, text, and video inputs within a 256K token context window.
During development, the team prioritized enhancing the reliability and efficiency of large models in real-world tasks. Addressing industry concerns that adding visual capabilities might degrade text intelligence, training results proved otherwise: native multimodal joint training not only expanded application boundaries but also further boosted text intelligence.
To ensure precise execution, Ling-3.0-flash-VL introduces an innovative visual feedback mechanism. By implementing a closed-loop process of observing results, comparing them with goals, identifying deviations, and continuously correcting, the model shifts from static generation to dynamic self-correction, significantly improving result reliability.
Furthermore, the model retains Ling-3.0-flash’s core strength as an efficient execution node in Agent workflows. Within visual feedback loops, it perfectly balances output quality and execution efficiency, enabling complex tasks to be completed faster and at lower costs.
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The Ant Open Source team has officially launched Ling-3.0-flash-VL, the first native multimodal large model in the Bailing series. Built on the MoE architecture of Ling-3.0-flash, this model features 124B total parameters, with only 5.5B activated per inference. It natively supports image, text, and video inputs within a 256K token context window.
During development, the team prioritized enhancing the reliability and efficiency of large models in real-world tasks. Addressing industry concerns that adding visual capabilities might degrade text intelligence, training results proved otherwise: native multimodal joint training not only expanded application boundaries but also further boosted text intelligence.
To ensure precise execution, Ling-3.0-flash-VL introduces an innovative visual feedback mechanism. By implementing a closed-loop process of observing results, comparing them with goals, identifying deviations, and continuously correcting, the model shifts from static generation to dynamic self-correction, significantly improving result reliability.
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