XiaomiMiMo

MiMo-V2.6-Flash-MOPD

1. Introduction

How MOPD2 works

MOPD2 distills several domain-specialized teachers into the student on-policy. The teachers fall into two families: mixRL teachers, trained on verifiable tasks, and SFT teachers, trained on synthetic demonstrations for open-domain tasks where a reliable reward is hard to design. Three streams contribute to a single update:

Standard MOPD: mixRL teachers supervise full autonomous rollouts.
Teacher-Prefix OPD: prefixes come from teacher rollouts. A trajectory with k assistant turns yields k history prefixes, one per turn. The model generates a single new turn from each, and the teacher scores it against the same history.
SFT-Prefix OPD: prefixes come from SFT demonstrations. The demonstration supplies the history, and the model writes its own continuation.

Method details are in Technical Report §5.6.

Tool-call repetition

Following the release of MiMo-V2.6, tool-call repetition emerged as one of the most noticeable issues in agentic settings: the model would sometimes issue the same or highly similar tool calls repeatedly, consuming time and context without making progress. This checkpoint mitigates it.

Tool-call repetition rate on MiMo-V2.6-Flash before and after MOPD

Figure: response-level repetition rate on MiMo-V2.6-Flash, RL-stage versus this checkpoint, across context lengths and agent harnesses.

The technical blog has the full diagnosis. The fix is lightweight to train: a short specialized-teacher run that folds into the normal MOPD pass.

Model Summary

Architecture: Sparse MoE (Mixture of Experts), 309B total / 15B activated parameters
Context Length: 1M tokens
Modalities: Text, Image, Video, Audio
Vision Encoder: 681M-param MiMo ViT (28 layers: 24 SWA + 4 Full)
Audio Encoder: 308M AudioTokenizer + 127M audio patch encoder
Multi-Token Prediction (MTP): 5-layer speculative decoder

Figure 1: MiMo-V2.6 architecture — omni encoders, hybrid SWA backbone, and MTP blocks

Figure 1. MiMo-V2.6 architecture.

2. Downloads

Table with columns: Model, Download
Model	Download
MiMo-V2.6-Pro-RL	🤗 HuggingFace · 🤖 ModelScope
MiMo-V2.6-Flash-RL	🤗 HuggingFace · 🤖 ModelScope
MiMo-V2.6-Pro-MOPD	🤗 HuggingFace · 🤖 ModelScope

3. Model Architecture

LLM Backbone

Table with columns: Component, MiMo-V2.6-Flash-MOPD
Component	MiMo-V2.6-Flash-MOPD
Layers (Total / SWA / GA)	48 / 39 / 9
Hidden Size	4096
SWA Heads (Q/KV)	64 / 8
GA Heads (Q/KV)	64 / 4
Head Dimensions (QK / V)	192 / 128
Sliding Window Size	128
Routed Experts (Total / Activated)	256 / 8
Max Context Length	1M
MTP / Speculative Decoder

The first Transformer block uses global attention with a dense FFN. Remaining blocks interleave local SWA and GA; both use sparse MoE FFNs without shared experts.

Vision Encoder (MiMo ViT)

Table with columns: Configuration, Value
Configuration	Value
Layers (Total / SWA / GA)	28 / 24 / 4
Hidden Size	1280
Attention Heads (Q / KV)	32 / 8
Head Dimension	64
Patch Size (T × H × W)	2 × 16 × 16
Sliding Window (Left / Right)	64 / 64
Spatial Merge Size	2 × 2
Parameters	681M

Audio Encoders

AudioTokenizer encoder: 24 layers (12 SWA / 12 GA), hidden 1024, 20 RVQ codebooks, 308M parameters. Audio patch encoder: 6 layers, 127M parameters; four frames per patch (25 Hz → 6.25 Hz).

Speculative Decoder

5-layer SWA MTP drafter (DFlash-style). Predicts 7 subsequent tokens per forward pass for parallel verification.

4. Deployment

For best performance, follow the SGLang MiMo cookbook. Docker image: lmsysorg/sglang:latest.

SGLang

bash
sglang serve \
  --trust-remote-code \
  --model-path XiaomiMiMo/MiMo-V2.6-Flash-MOPD \
  --tp 8 \
  --dp 2 \
  --enable-dp-attention \
  --enable-dp-lm-head \
  --mm-enable-dp-encoder \
  --mem-fraction-static 0.65 \
  --chunked-prefill-size 16384 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --enable-multi-layer-eagle \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --host 0.0.0.0 \
  --port 30000

vLLM

Follow the vLLM MiMo-V2.5 recipe. Stable vLLM may lag; pre-built image: docker pull vllm/vllm-openai:mimov25-cu129.

bash
vllm serve XiaomiMiMo/MiMo-V2.6-Flash-MOPD \
  --tensor-parallel-size 4 \
  --trust-remote-code \
  --gpu-memory-utilization 0.95 \
  --max-model-len auto \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --enable-auto-tool-choice \
  --generation-config vllm

Recommended sampling: temperature=1.0, top_p=0.95.

Also available in AI Studio, MiMo Code, Xiaomi MiMo Desktop, Xiaomi MiMo Open Platform API, and OpenRouter.

Citation

bibtex
@misc{mimo2026v26flashmopd,
  title={MiMo-V2.6-Flash-MOPD},
  author={{Xiaomi MiMo Team}},
  year={2026},
  howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD}},
}

Contact

For questions or feedback, reach us at mimo@xiaomi.com or join our community:

Available on FriendliAI

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Model Details

Model Provider

XiaomiMiMo

Model Tree

Base

this model

Input Modalities

TextAudioImageVideo

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

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1. Introduction

How MOPD2 works

Standard MOPD: mixRL teachers supervise full autonomous rollouts.
Teacher-Prefix OPD: prefixes come from teacher rollouts. A trajectory with k assistant turns yields k history prefixes, one per turn. The model generates a single new turn from each, and the teacher scores it against the same history.
SFT-Prefix OPD: prefixes come from SFT demonstrations. The demonstration supplies the history, and the model writes its own continuation.

Method details are in Technical Report §5.6.

Tool-call repetition

Tool-call repetition rate on MiMo-V2.6-Flash before and after MOPD

Figure: response-level repetition rate on MiMo-V2.6-Flash, RL-stage versus this checkpoint, across context lengths and agent harnesses.

The technical blog has the full diagnosis. The fix is lightweight to train: a short specialized-teacher run that folds into the normal MOPD pass.

Model Summary

Architecture: Sparse MoE (Mixture of Experts), 309B total / 15B activated parameters
Context Length: 1M tokens
Modalities: Text, Image, Video, Audio
Vision Encoder: 681M-param MiMo ViT (28 layers: 24 SWA + 4 Full)
Audio Encoder: 308M AudioTokenizer + 127M audio patch encoder
Multi-Token Prediction (MTP): 5-layer speculative decoder

Figure 1: MiMo-V2.6 architecture — omni encoders, hybrid SWA backbone, and MTP blocks

Figure 1. MiMo-V2.6 architecture.

2. Downloads

Table with columns: Model, Download
Model	Download
MiMo-V2.6-Pro-RL	🤗 HuggingFace · 🤖 ModelScope
MiMo-V2.6-Flash-RL	🤗 HuggingFace · 🤖 ModelScope
MiMo-V2.6-Pro-MOPD	🤗 HuggingFace · 🤖 ModelScope

3. Model Architecture

LLM Backbone

Table with columns: Component, MiMo-V2.6-Flash-MOPD
Component	MiMo-V2.6-Flash-MOPD
Layers (Total / SWA / GA)	48 / 39 / 9
Hidden Size	4096
SWA Heads (Q/KV)	64 / 8
GA Heads (Q/KV)	64 / 4
Head Dimensions (QK / V)	192 / 128
Sliding Window Size	128
Routed Experts (Total / Activated)	256 / 8
Max Context Length	1M
MTP / Speculative Decoder

The first Transformer block uses global attention with a dense FFN. Remaining blocks interleave local SWA and GA; both use sparse MoE FFNs without shared experts.

Vision Encoder (MiMo ViT)

Table with columns: Configuration, Value
Configuration	Value
Layers (Total / SWA / GA)	28 / 24 / 4
Hidden Size	1280
Attention Heads (Q / KV)	32 / 8
Head Dimension	64
Patch Size (T × H × W)	2 × 16 × 16
Sliding Window (Left / Right)	64 / 64
Spatial Merge Size	2 × 2
Parameters	681M

Audio Encoders

AudioTokenizer encoder: 24 layers (12 SWA / 12 GA), hidden 1024, 20 RVQ codebooks, 308M parameters. Audio patch encoder: 6 layers, 127M parameters; four frames per patch (25 Hz → 6.25 Hz).

Speculative Decoder

5-layer SWA MTP drafter (DFlash-style). Predicts 7 subsequent tokens per forward pass for parallel verification.

4. Deployment

For best performance, follow the SGLang MiMo cookbook. Docker image: lmsysorg/sglang:latest.

SGLang

bash
sglang serve \
  --trust-remote-code \
  --model-path XiaomiMiMo/MiMo-V2.6-Flash-MOPD \
  --tp 8 \
  --dp 2 \
  --enable-dp-attention \
  --enable-dp-lm-head \
  --mm-enable-dp-encoder \
  --mem-fraction-static 0.65 \
  --chunked-prefill-size 16384 \
  --speculative-algorithm EAGLE \
  --speculative-num-steps 3 \
  --speculative-eagle-topk 1 \
  --speculative-num-draft-tokens 4 \
  --enable-multi-layer-eagle \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --host 0.0.0.0 \
  --port 30000

vLLM

Follow the vLLM MiMo-V2.5 recipe. Stable vLLM may lag; pre-built image: docker pull vllm/vllm-openai:mimov25-cu129.

bash
vllm serve XiaomiMiMo/MiMo-V2.6-Flash-MOPD \
  --tensor-parallel-size 4 \
  --trust-remote-code \
  --gpu-memory-utilization 0.95 \
  --max-model-len auto \
  --reasoning-parser mimo \
  --tool-call-parser mimo \
  --enable-auto-tool-choice \
  --generation-config vllm

Recommended sampling: temperature=1.0, top_p=0.95.

Also available in AI Studio, MiMo Code, Xiaomi MiMo Desktop, Xiaomi MiMo Open Platform API, and OpenRouter.

Citation

bibtex
@misc{mimo2026v26flashmopd,
  title={MiMo-V2.6-Flash-MOPD},
  author={{Xiaomi MiMo Team}},
  year={2026},
  howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-MOPD}},
}

Contact

For questions or feedback, reach us at mimo@xiaomi.com or join our community:

MiMo-V2.6-Flash-MOPD

README

1. Introduction

How MOPD2 works

Tool-call repetition

Model Summary

2. Downloads

3. Model Architecture

LLM Backbone

Vision Encoder (MiMo ViT)

Audio Encoders

Speculative Decoder

4. Deployment

SGLang

vLLM

Citation

Contact

Explore FriendliAI today

README

1. Introduction

How MOPD2 works

Tool-call repetition

Model Summary

2. Downloads

3. Model Architecture

LLM Backbone

Vision Encoder (MiMo ViT)

Audio Encoders

Speculative Decoder

4. Deployment

SGLang

vLLM

Citation

Contact