Text / XiaomiMiMo

MiMo V2.6 Flash RL

MiMo V2.6 Flash RL is a text-generation model from XiaomiMiMo, released under the MIT license and associated with safetensors and text-generation tags. Its official model card describes it as the efficiency-balanced checkpoint of the MiMo-V2.6 series, built to scale reinforcement learning toward self-improvement across text, image, video, and audio, with a stated 1M-token long-context capability.

EstimatedRepository opened Sep 21, 2026Source checked 9/23/2026Version: 5711b268
LOCALRENTED GPUOPEN WEIGHTS

At a glance

Parameters
310.76B
Architecture
mimo_v2
Context length
1,048,576
License
mit
Software
SGLang, vLLM
View the model source
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Best for

  • Text generation
  • Multimodal and vision-language tasks
  • Video understanding
  • Audio-related tasks
  • Agent and long-context scenarios

Tradeoffs

  • The source provides no information about hardware requirements or compatibility.
  • The source provides no performance benchmarks or comparative evaluations.
  • The source provides no deployment instructions or supported inference frameworks.
  • The source does not specify the model architecture, parameter count, or training data.
  • The source does not describe safety, bias, or content-filtering behavior.

Ways to run it

SGLang · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

sglang serve --trust-remote-code --model-path XiaomiMiMo/MiMo-V2.6-Flash-RL --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 \
Official instructions

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

vllm serve XiaomiMiMo/MiMo-V2.6-Flash-RL --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
Official instructions

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