Xiaomi MiMo

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Xiaomi MiMo
@XiaomiMiMo

Xiaomi MiMo’s posts

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Xiaomi MiMo-V2.5 is now officially open-sourced! MIT License, supporting commercial deployment, continued training, and fine-tuning - no additional authorization required. Two models, both supporting a 1M-token context window : • MiMo-V2.5-Pro: built for complex agent and
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👋 Say Hi to MiMo-Audio! Our BREAKTHROUGH in general-purpose audio intelligence. 🎯 Scaling pretraining to 100M+ hours leads to EMERGENCE of few-shot generalization across diverse audio tasks! 🔥 Post-trained MiMo-Audio-7B-Instruct: • crushes benchmarks: SOTA on MMSU, MMAU,
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🚀 MiMo‑VL 2508 is live! Same size, much smarter 🚀 We’ve upgraded performance, thinking control, and overall user experience. 📈 Benchmark gains across image + video: MMMU 70.6, VideoMME 70.8. Consistent improvements across the board. 🤖 Thinking Control: toggle reasoning
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Today, MiMo can see We release MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language models delivering state-of-the-art performance in both general visual understanding and multimodal reasoning. MiMo-VL-7B-RL outperforms Qwen2.5-VL-7B on 35 out of 40 evaluated tasks,
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Along side the remark MiMo-VL series, we also present MiMo-7B-RL-0530, which has seen significant improvements in reasoning and general capabilities through continuous reinforcement learning (RL) after the initial open-source release of MiMo-7B. In multiple mathematical coding
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Today, Xiaomi releases MiMo, our first open-source reasoning model. At 7B parameters, it’s optimized for reasoning through pre-training and post-training, surpassing OpenAI’s o1-mini and QwQ-32B-Preview on AIME 2024-2025 and LiveCodeBench v5 benchmarks. #XiaomiMiMo #MiMo7B
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Thanks for sharing! We have a detailed introductory post here:
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Xiaomi MiMo
@XiaomiMiMo
Today, MiMo can see We release MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language models delivering state-of-the-art performance in both general visual understanding and multimodal reasoning. MiMo-VL-7B-RL outperforms Qwen2.5-VL-7B on 35 out of 40 evaluated tasks,
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During the journey, we encountered two important observations that are worth sharing: (1) Incorporating high-quality, broad-coverage reasoning data from the pre-training stage is crucial for enhancing model performance. (2) Mixed On-policy Reinforcement Learning further
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MiMo-7B-RL not only excels in code and algorithmic tasks but also outperforms both QwQ-32B-Preview and DeepSeek-R1-Distill-Qwen-7B across general tasks, even when the reinforcement learning evaluation is limited to mathematics and code problems.
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What can MiMO-VL-RL do? 🤔 In Case #1, our model showcases strong plot understanding capabilities, successfully converting an intricate plot into a well-structured markdown table. It also demonstrates superior reasoning capabilities in STEM tasks. In Case #2, MiMo-VL-7B
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On general benchmarks, the MiMo-VL-7B models, particularly MiMo-VL-7B-SFT and MiMo-VL-7B-RL, demonstrate consistently leading performance across a diverse range of vision-language benchmarks, surpassing other open-source models of comparable or larger scale.
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On GUI understanding and grounding tasks, as a general-purpose VLM, MiMo-VL achieves comparable or even superior performance to GUI-specialized models, particularly on the more challenging Screenspot-Pro and OSWorld-G benchmarks.
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The comprehensive visual perception capabilities of MiMo-VL-7B are attributed to high-quality pre-training data and an innovative Mixed On-policy Reinforcement Learning (MORL) algorithm: Multi-Stage Pre-training: High-quality pre-training multimodal data has been collected,
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Thanks for sharing! We have a detailed introductory post here:
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Xiaomi MiMo
@XiaomiMiMo
Today, Xiaomi releases MiMo, our first open-source reasoning model. At 7B parameters, it’s optimized for reasoning through pre-training and post-training, surpassing OpenAI’s o1-mini and QwQ-32B-Preview on AIME 2024-2025 and LiveCodeBench v5 benchmarks. #XiaomiMiMo #MiMo7B
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MiMo-7B-Base achieves significantly higher pass@k scores in reasoning benchmarks than all compared models, including the 32B baseline, across all benchmarks and evaluated k values. These results highlight the exceptional reasoning capabilities of MiMo-7B-Base.
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