Instructions to use XiaomiMiMo/MiMo-V2.6-Flash-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XiaomiMiMo/MiMo-V2.6-Flash-RL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2.6-Flash-RL", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("XiaomiMiMo/MiMo-V2.6-Flash-RL", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XiaomiMiMo/MiMo-V2.6-Flash-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaomiMiMo/MiMo-V2.6-Flash-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2.6-Flash-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XiaomiMiMo/MiMo-V2.6-Flash-RL

SGLang

How to use XiaomiMiMo/MiMo-V2.6-Flash-RL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "XiaomiMiMo/MiMo-V2.6-Flash-RL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2.6-Flash-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "XiaomiMiMo/MiMo-V2.6-Flash-RL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2.6-Flash-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XiaomiMiMo/MiMo-V2.6-Flash-RL with Docker Model Runner:
```
docker model run hf.co/XiaomiMiMo/MiMo-V2.6-Flash-RL
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Community
WeChat Group | Discord | Telegram | Reddit

MiMo-V2.6-Flash-RL

Scaling Reinforcement Learning Toward Self-Improvement

Technical Report

1. Introduction

MiMo-V2.6-Flash-RL is the efficiency-balanced checkpoint of the MiMo-V2.6 series. The series is built to scale reinforcement learning toward self-improvement — scaling RL compute, environment diversity, and grader compute together, so the model keeps expanding its capability frontier through exploration and feedback. Key features include:

Native Omnimodal + Long Horizon: Text, image, video, and audio in one model; 1M tokens for long repositories, tool traces, and multi-session agent runs.
You Only RL Once: One mixed RL run across coding, general agents, visual, and cybersecurity — not separate per-domain runs. Tasks and multiple harnesses are mixed in the same batch so capabilities reinforce each other and strategies transfer to harnesses never seen in training.
Scaling RL Compute: Fully asynchronous Group Relative Policy Optimization (GRPO) on very large batches — 1,568 prompts × 16 rollouts per step, billions of tokens per update.
Groupwise Agentic Grading (Self-Improvement Loop): Binary pass/fail cannot rank passing solutions, so the reward signal itself is scaled. An agentic grader compares rollouts within each group: Groupwise Reward Synthesis (GRS) builds task-specific rubrics offline from contrasting rollouts and fuses rubric quality with test outcomes; Groupwise Advantage Redistribution (GAR) ranks passing trajectories online and moves advantage toward higher-quality solutions. Judged against the policy’s own samples, this closes a self-improvement loop and steers toward shorter paths and fewer tokens per task.
Aligned RL: Cold start from self-correction — the model reflects on and rewrites its own misaligned turns into grounded next steps. Throughout RL, environment hardening, adversarial screening, and verifier cross-checks keep the loop honest against reward hacking.
Multi-Prefix Multi-Teacher On-Policy Distillation (MOPD2): After mixed RL, MOPD2 combines autonomous student rollouts with prefix-conditioned single-turn rollouts (Teacher-Prefix and SFT-Prefix), reusing histories from teacher trajectories and SFT demonstrations so decision points train without regenerating preceding turns — extending capabilities to hard-to-verify tasks.

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.

2. Downloads

Model	Download
MiMo-V2.6-Pro-RL	🤗 HuggingFace · 🤖 ModelScope
MiMo-V2.6-Flash-RL	🤗 HuggingFace · 🤖 ModelScope

3. Evaluation Results

Benchmark	MiMo-V2.6 Pro	MiMo-V2.6 Flash	MiMo-V2.5 Pro	Claude Opus 5	GPT-5.6 Sol	Claude Fable 5
Code Agent
DeepSWE v1.1	71.9	67.9	19.0	74.0	73.0	70.0
ProgramBench	26.5	26.0	12.5	37.0	25.0	33.0
MiMo Code Bench	63.2	61.2	40.4	68.6	59.3	-
General Agent
AutomationBench v1.0.6	53.1	52.3	16.0	50.3	45.8	46.2
Toolathlon-Verified	76.9	73.6	49.1	80.6	74.9	77.9
GDPval-AA 2.1	1673	-	1107	1708	1588	1595
Agents’ Last Exam	31.6	27.6	13.2	31.6	30.8	25.7
Terminal Bench 4.0	34.9	28.8	1.5	49.0	39.9	42.4
Terminal Bench 2.1	89.9	87.6	65.2	89.1	88.8	84.3
OSWorld-Verified	82.0	80.8	-	83.4	83.0	86.0
JobBench	62.0	61.2	25.0	65.7	45.4	57.4
Cybersecurity
CyberGym	94.0	95.1	40.0	-	-	-
MiMo Cyber Bench	80.2	77.2	0.0	-	-	-
ExploitGym	17.8	6.0	0.2	22.1	30.3	28.4
ExploitBench	47.9	25.3	16.6	70.0	78.5	78.0
SEC Bench Pro	66.3	47.5	17.7	-	79.1	-
Visual Agent
MiMo VisualCoding	72.3	71.5	-	70.0	73.4	69.1

4. Model Architecture

LLM Backbone

Component	MiMo-V2.6-Flash-RL
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	5 SWA layers, window 1024

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)

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.

5. Deployment

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

SGLang

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 \
  --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.

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

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

@misc{mimo2026v26flash,
  title={MiMo-V2.6-Flash-RL},
  author={{Xiaomi MiMo Team}},
  year={2026},
  howpublished={\url{https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL}},
}

Contact

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

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