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--- |
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license: mit |
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base_model: |
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- XiaomiMiMo/MiMo-V2-Flash-Base |
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library_name: transformers |
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--- |
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<br/><br/> |
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<div align="center"> |
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<picture> |
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<source srcset="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)"> |
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<img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/Xiaomi_MiMo.png?raw=true" width="60%" alt="Xiaomi-MiMo" /> |
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</picture> |
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</div> |
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<br/> |
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<div align="center" style="line-height: 1;"> |
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<a href="https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash" target="_blank">🤗 HuggingFace</a> |
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<a href="https://github.com/XiaomiMiMo/MiMo-V2-Flash/blob/main/paper.pdf" target="_blank">📔 Technical Report </a> |
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<a href="https://mimo.xiaomi.com/blog/mimo-v2-flash" target="_blank">📰 Blog </a> |
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<br/><br/> |
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<strong>Play around!</strong> |
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<a href="https://aistudio.xiaomimimo.com" target="_blank">🗨️ Xiaomi MiMo Studio </a> |
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<a href="https://platform.xiaomimimo.com/" target="_blank">🎨 Xiaomi MiMo API Platform </a> |
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</div> |
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<br/> |
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# MiMo-V2-Flash |
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**MiMo-V2-Flash** is a Mixture-of-Experts (MoE) language model with **309B total parameters** and **15B active parameters**. Designed for high-speed reasoning and agentic workflows, it utilizes a novel hybrid attention architecture and Multi-Token Prediction (MTP) to achieve state-of-the-art performance while significantly reducing inference costs. |
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<p align="center"> |
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<img width="80%" src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/MiMo-v2-flash-performance.jpg?raw=true"> |
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</p> |
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----- |
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## 1. Introduction |
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MiMo-V2-Flash creates a new balance between long-context modeling capability and inference efficiency. Key features include: |
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* **Hybrid Attention Architecture**: Interleaves Sliding Window Attention (SWA) and Global Attention (GA) with a 5:1 ratio and an aggressive 128-token window. This reduces KV-cache storage by nearly 6x while maintaining long-context performance via learnable **attention sink bias**. |
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* **Multi-Token Prediction (MTP)**: Equipped with a lightweight MTP module (0.33B params/block) using dense FFNs. This triples output speed during inference and will be good to accelerates rollout in RL training. |
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* **Efficient Pre-Training**: Trained on 27T tokens using FP8 mixed precision and native 32k seq length. The context window supports up to 256k length. |
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* **Agentic Capabilities**: Post-training utilizes Multi-Teacher On-Policy Distillation (MOPD) and large-scale agentic RL, achieving superior performance on **SWE-Bench** and complex reasoning tasks. |
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----- |
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## 2. Model Downloads |
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| Model | Total Params | Active Params | Context Length | Download | |
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| :--------------------- | :----------: | :-----------: | :------------: | :-------------------------------------------------------------------: | |
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| **MiMo-V2-Flash-Base** | 309B | 15B | 256k | [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash-Base) | |
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| **MiMo-V2-Flash** | 309B | 15B | 256k | [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash) | |
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> [!IMPORTANT] |
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> We also open-source the 3-layer MTP weights to foster community research. |
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----- |
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## 3. Evaluation Results |
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### Base Model Evaluation |
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MiMo-V2-Flash-Base demonstrates strong performance across standard benchmarks, surpassing models with significantly larger parameter counts. |
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| Category | Benchmark | Setting/Length | MiMo-V2-Flash Base | Kimi-K2 Base | DeepSeek-V3.1 Base | DeepSeek-V3.2 Exp Base | |
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| :--------------- | :---------------------- | :------------- | :----------------: | :-------------: | :----------------: | :--------------------: | |
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| **Params** | **#Activated / #Total** | - | **15B / 309B** | **32B / 1043B** | **37B / 671B** | **37B / 671B** | |
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| **General** | BBH | 3-shot | 88.5 | 88.7 | 88.2 | 88.7 | |
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| | MMLU | 5-shot | 86.7 | 87.8 | 87.4 | 87.8 | |
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| | MMLU-Redux | 5-shot | 90.6 | 90.2 | 90.0 | 90.4 | |
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| | MMLU-Pro | 5-shot | 73.2 | 69.2 | 58.8 | 62.1 | |
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| | DROP | 3-shot | 84.7 | 83.6 | 86.3 | 86.6 | |
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| | ARC-Challenge | 25-shot | 95.9 | 96.2 | 95.6 | 95.5 | |
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| | HellaSwag | 10-shot | 88.5 | 94.6 | 89.2 | 89.4 | |
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| | WinoGrande | 5-shot | 83.8 | 85.3 | 85.9 | 85.6 | |
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| | TriviaQA | 5-shot | 80.3 | 85.1 | 83.5 | 83.9 | |
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| | GPQA-Diamond | 5-shot | 55.1 | 48.1 | 51.0 | 52.0 | |
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| | SuperGPQA | 5-shot | 41.1 | 44.7 | 42.3 | 43.6 | |
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| | SimpleQA | 5-shot | 20.6 | 35.3 | 26.3 | 27.0 | |
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| **Math** | GSM8K | 8-shot | 92.3 | 92.1 | 91.4 | 91.1 | |
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| | MATH | 4-shot | 71.0 | 70.2 | 62.6 | 62.5 | |
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| | AIME 24&25 | 2-shot | 35.3 | 31.6 | 21.6 | 24.8 | |
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| **Code** | HumanEval+ | 1-shot | 70.7 | 84.8 | 64.6 | 67.7 | |
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| | MBPP+ | 3-shot | 71.4 | 73.8 | 72.2 | 69.8 | |
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| | CRUXEval-I | 1-shot | 67.5 | 74.0 | 62.1 | 63.9 | |
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| | CRUXEval-O | 1-shot | 79.1 | 83.5 | 76.4 | 74.9 | |
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| | MultiPL-E HumanEval | 0-shot | 59.5 | 60.5 | 45.9 | 45.7 | |
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| | MultiPL-E MBPP | 0-shot | 56.7 | 58.8 | 52.5 | 50.6 | |
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| | BigCodeBench | 0-shot | 70.1 | 61.7 | 63.0 | 62.9 | |
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| | LiveCodeBench v6 | 1-shot | 30.8 | 26.3 | 24.8 | 24.9 | |
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| | SWE-Bench (AgentLess) | 3-shot | 30.8 | 28.2 | 24.8 | 9.4* | |
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| **Chinese** | C-Eval | 5-shot | 87.9 | 92.5 | 90.0 | 91.0 | |
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| | CMMLU | 5-shot | 87.4 | 90.9 | 88.8 | 88.9 | |
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| | C-SimpleQA | 5-shot | 61.5 | 77.6 | 70.9 | 68.0 | |
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| **Multilingual** | GlobalMMLU | 5-shot | 76.6 | 80.7 | 81.9 | 82.0 | |
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| | INCLUDE | 5-shot | 71.4 | 75.3 | 77.2 | 77.2 | |
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| **Long Context** | NIAH-Multi | 32K | 99.3 | 99.8 | 99.7 | 85.6* | |
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| | | 64K | 99.9 | 100.0 | 98.6 | 85.9* | |
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| | | 128K | 98.6 | 99.5 | 97.2 | 94.3* | |
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| | | 256K | 96.7 | - | - | - | |
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| | GSM-Infinite Hard | 16K | 37.7 | 34.6 | 41.5 | 50.4 | |
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| | | 32K | 33.7 | 26.1 | 38.8 | 45.2 | |
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| | | 64K | 31.5 | 16.0 | 34.7 | 32.6 | |
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| | | 128K | 29.0 | 8.8 | 28.7 | 25.7 | |
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> \* indicates the model may fail to follow the prompt or format. |
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### Post-training Model Evaluation |
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Following our Post-Training Paradigm with MOPD and Agentic RL, the model achieves SOTA reasoning and agentic performance. |
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| Benchmark | MiMo-V2 Flash | Kimi-K2 Thinking | DeepSeek-V3.2 Thinking | Gemini-3.0 Pro | Claude Sonnet 4.5 | GPT-5 High | |
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| :----------------------------- | :-----------: | :--------------: | :--------------------: | :------------: | :---------------: | :--------: | |
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| **Reasoning** | | | | | | | |
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| MMLU-Pro | 84.9 | 84.6 | 85.0 | 90.1 | 88.2 | 87.5 | |
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| GPQA-Diamond | 83.7 | 84.5 | 82.4 | 91.9 | 83.4 | 85.7 | |
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| HLE (no tools) | 22.1 | 23.9 | 25.1 | 37.5 | 13.7 | 26.3 | |
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| AIME 2025 | 94.1 | 94.5 | 93.1 | 95.0 | 87.0 | 94.6 | |
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| HMMT Feb. 2025 | 84.4 | 89.4 | 92.5 | 97.5 | 79.2 | 88.3 | |
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| LiveCodeBench-v6 | 80.6 | 83.1 | 83.3 | 90.7 | 64.0 | 84.5 | |
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| **General Writing** | | | | | | | |
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| Arena-Hard (Hard Prompt) | 54.1 | 71.9 | 53.4 | 72.6 | 63.3 | 71.9 | |
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| Arena-Hard (Creative Writing) | 86.2 | 80.1 | 88.8 | 93.6 | 76.7 | 92.2 | |
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| **Long Context** | | | | | | | |
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| LongBench V2 | 60.6 | 45.1 | 58.4 | 65.6 | 61.8 | - | |
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| MRCR | 45.7 | 44.2 | 55.5 | 89.7 | 55.4 | - | |
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| **Code Agent** | | | | | | | |
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| SWE-Bench Verified | 73.4 | 71.3 | 73.1 | 76.2 | 77.2 | 74.9 | |
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| SWE-Bench Multilingual | 71.7 | 61.1 | 70.2 | - | 68.0 | 55.3 | |
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| Terminal-Bench Hard | 30.5 | 30.6 | 35.4 | 39.0 | 33.3 | 30.5 | |
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| Terminal-Bench 2.0 | 38.5 | 35.7 | 46.4 | 54.2 | 42.8 | 35.2 | |
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| **General Agent** | | | | | | | |
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| BrowseComp | 45.4 | - | 51.4 | - | 24.1 | 54.9 | |
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| BrowseComp (w/ Context Manage) | 58.3 | 60.2 | 67.6 | 59.2 | - | - | |
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| \\(\tau^2\\)-Bench | 80.3 | 74.3 | 80.3 | 85.4 | 84.7 | 80.2 | |
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----- |
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## 4. Model Architecture |
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<p align="center"> |
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<img width="80%" src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/MiMo-v2-flash-arch.png?raw=true"> |
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</p> |
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### Hybrid Sliding Window Attention |
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MiMo-V2-Flash addresses the quadratic complexity of long contexts by interleaving Local Sliding Window Attention (SWA) and Global Attention (GA). |
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* **Configuration**: Stacks of \\(M=8\\) hybrid blocks. Each block contains \\(N=5\\) SWA layers followed by 1 GA layer. |
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* **Efficiency**: SWA layers use a window size of 128 tokens, reducing KV cache significantly. |
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* **Sink Bias**: Learnable attention sink bias is applied to maintain performance despite the aggressive window size. |
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### Lightweight Multi-Token Prediction (MTP) |
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Unlike traditional speculative decoding, our MTP module is natively integrated for training and inference. |
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* **Structure**: Uses a dense FFN (instead of MoE) and SWA (instead of GA) to keep the parameter count low (0.33B per block). |
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* **Performance**: Facilitates self-speculative decoding, tripling generation speed and mitigating GPU idleness during small-batch RL training. |
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----- |
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## 5. Post-Training Technical Highlights |
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MiMo-V2-Flash leverages a post-training pipeline designed to maximize reasoning and agentic capabilities through innovative distillation and reinforcement learning strategies. |
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### 5.1 Multi-Teacher On-Policy Distillation (MOPD) |
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We introduce **Multi-Teacher On-Policy Distillation (MOPD)**, a new paradigm that formulates knowledge distillation as a reinforcement learning process. |
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* **Dense Token-Level Guidance**: Unlike methods relying on sparse sequence-level feedback, MOPD utilizes domain-specific expert models (teachers) to provide supervision at every token position. |
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* **On-Policy Optimization**: The student model learns from its own generated responses rather than a fixed dataset. This eliminates exposure bias and ensures smaller, more stable gradient updates. |
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* **Inherent Reward Robustness**: Rewards are derived from the distribution divergence between student and teacher, making the process naturally resistant to reward hacking. |
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### 5.2 Scaling Agentic RL |
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We significantly scale up the agentic training environments to improve intelligence and generalization. |
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* **Massive Code Agent Environments**: We utilize real-world GitHub issues to create over 100,000 verifiable tasks. Our automated pipeline maintains a Kubernetes cluster capable of running over 10,000 concurrent pods with a 70% environment setup success rate. |
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* **Multimodal Verifier for WebDev**: For web development tasks, we employ a vision-based verifier that evaluates code execution via recorded videos rather than static screenshots. This reduces visual hallucination and ensures functional correctness. |
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* **Cross-Domain Generalization**: Our experiments show that large-scale RL training on code agents effectively generalizes to other domains, boosting performance in Math and General Agent tasks. |
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### 5.3 Advanced RL Infrastructure |
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To support high-throughput RL training for large-scale MoE models, we implemented several infrastructure optimizations on top of SGLang and Megatron-LM. |
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* **Rollout Routing Replay (R3)**: Addresses numerical precision inconsistencies in MoE routing between inference and training. R3 reuses the exact routed experts from rollout during the training pass, ensuring consistency with negligible overhead. |
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* **Request-Level Prefix Cache**: In multi-turn agent training, this cache stores KV states and routed experts from prior turns. It avoids re-computation and ensures sampling consistency across turns. |
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* **Fine-Grained Data Scheduler**: We extend the rollout engine to schedule fine-grained sequences instead of micro-batches. Combined with partial rollout, this significantly reduces GPU idleness caused by long-tail stragglers. |
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* **Toolbox & Tool Manager**: A two-layer design using Ray actor pools to handle resource contention. It eliminates cold-start delays for tool execution and isolates task logic from system policies. |
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----- |
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## 6. Inference & Deployment |
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MiMo-V2-Flash supports FP8 mixed precision inference. We recommend using **SGLang** for optimal performance. |
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Usage Recommendations: we recommend setting the sampling parameters to `temprature=0.8, top_p=0.95`. |
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### Quick Start with SGLang |
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```bash |
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pip install sglang |
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# Launch server |
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python3 -m sglang.launch_server \ |
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--model-path XiaomiMiMo/MiMo-V2-Flash \ |
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--served-model-name mimo-v2-flash \ |
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--pp-size 1 \ |
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--dp-size 2 \ |
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--enable-dp-attention \ |
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--tp-size 8 \ |
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--moe-a2a-backend deepep \ |
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--page-size 1 \ |
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--host 0.0.0.0 \ |
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--port 9001 \ |
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--trust-remote-code \ |
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--mem-fraction-static 0.75 \ |
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--max-running-requests 128 \ |
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--chunked-prefill-size 16384 \ |
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--reasoning-parser qwen3 \ |
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--tool-call-parser mimo \ |
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--context-length 262144 \ |
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--attention-backend fa3 \ |
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--speculative-algorithm EAGLE \ |
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--speculative-num-steps 3 \ |
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--speculative-eagle-topk 1 \ |
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--speculative-num-draft-tokens 4 \ |
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--enable-mtp |
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# Send request |
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curl -i http://localhost:9001/v1/chat/completions \ |
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-H 'Content-Type:application/json' \ |
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-d '{ |
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"messages" : [{ |
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"role": "user", |
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"content": "Nice to meet you MiMo" |
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}], |
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"model": "mimo-v2-flash", |
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"max_tokens": 4096, |
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"temperature": 0.8, |
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"top_p": 0.95, |
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"stream": true, |
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"chat_template_kwargs": { |
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"enable_thinking": true |
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} |
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}' |
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``` |
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### Notifications |
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#### 1. System prompt |
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> [!IMPORTANT] |
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> The following system prompts are **HIGHLY** recommended, please choose from English and Chinese version. |
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English |
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```plaintext |
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You are MiMo, an AI assistant developed by Xiaomi. |
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Today's date: {date} {week}. Your knowledge cutoff date is December 2024. |
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``` |
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Chinese |
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```plaintext |
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你是MiMo（中文名称也是MiMo），是小米公司研发的AI智能助手。 |
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今天的日期：{date} {week}，你的知识截止日期是2024年12月。 |
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``` |
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#### 2. Sampling parameters |
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> [!IMPORTANT] |
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> Recommended sampling parameters: |
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> |
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> `top_p=0.95` |
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> |
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> `temperature=0.8` for math, writing, web-dev |
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> |
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> `temperature=0.3` for agentic taks (e.g., vibe-coding, tool-use) |
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#### 3. Tool-use practice |
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> [!IMPORTANT] |
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> In the thinking mode with multi-turn tool calls, the model returns a `reasoning_content` field alongside `tool_calls`. To continue the conversation, the user must persist all history `reasoning_content` in the `messages` array of each subsequent request. |
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----- |
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## 7. Citation |
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If you find our work helpful, please cite our technical report: |
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```bibtex |
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@misc{mimo2025flash, |
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title={MiMo-V2-Flash Technical Report}, |
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author={LLM-Core Xiaomi}, |
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year={2025}, |
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url={https://github.com/XiaomiMiMo/MiMo-V2-Flash/paper.pdf} |
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} |
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``` |
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## 8. Contact |
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Please contact us at [mimo@xiaomi.com](mailto:mimo@xiaomi.com), join our WeChat group below or open an issue if you have any questions. |
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<p align="center"> |
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<img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/wechat_group/wechat1.jpg?raw=true" width="20%" /> |
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<img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/wechat_group/wechat2.jpg?raw=true" width="20%" /> |
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<img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/wechat_group/wechat3.jpg?raw=true" width="20%" /> |
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<img src="https://github.com/XiaomiMiMo/MiMo-V2-Flash/raw/main/figures/wechat_group/wechat4.jpg?raw=true" width="20%" /> |
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</p> |
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