Model overview
Ming-flash-omni Preview, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100B total parameters, of which only 6B are active per token. Compared to its predecessor, the upgraded version exhibits substantial improvements across multimodal understanding and generation. We significantly advance speech recognition capabilities, achieving state-of-the-art performance in both contextual ASR and dialect-aware ASR. In image generation, Ming-flash-omni Preview introduces high-fidelity text rendering and demonstrates marked gains in scene consistency and identity preservation during image editing. Furthermore, Ming-flash-omni Preview introduces generative segmentation, a capability that not only achieves strong standalone segmentation performance but also enhances spatial control in image generation and improves editing consistency. It demonstrates highly competitive results in various modal benchmarks compared to industry-leading models. Compared to Ming-lite-omni v1.5, Ming-flash-omni Preview features key optimizations in the following 3 areas: - **Sparse MoE Architecture for Omni-Modality**: The Sparse MoE Architecture for Omni-Modality features a 100B-A6B MoE backbone (an extension of Ling-Flash-2.0). To ensure uniform expert activation and stable training across all modalities, Ming-flash-omni Preview employs a Dual-Balanced Routing Mechanism that combines an Auxiliary Load Balancing Loss with a Modality-Level Router Bias Update. - **Generative Segmentation-as-Editing Paradigm**: It unifies segmentation and editing into a semantics-preserving generation task, and achieves $0.90$ on GenEval, surpassing non-RL methods in fine-grained spatial control. - **Context-Aware and Dialectal Speech Recognition**: Ming-flash-omni Preview sets a new State-of-the-Art performance across all 12 ContextASR benchmarks, and it significantly improves recognition performance for 15 Chinese dialects. You can download our latest model from both Huggingface and ModelScope. For previous version model like Ming-Lite-Omni v1.5, Please refer to this link.
Key features
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Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
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Sources & traceability
许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。