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")

Inference
HuggingChat
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
```

MiMo-V2.6-Flash-RL / README.md

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	---
	license: mit
	language:
	- en
	- zh
	tags:
	- text-generation
	- multimodal
	- vision-language
	- audio
	- agent
	- video-understanding
	- long-context
	- mimo_v2
	- transformers
	library_name: transformers
	---

	<br/><br/>

	<div align="center">
	<picture>
	<source srcset="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/Xiaomi_MiMo_darkmode.png?raw=true" media="(prefers-color-scheme: dark)">
	<img src="https://github.com/XiaomiMiMo/MiMo/raw/main/figures/Xiaomi_MiMo.png?raw=true" width="60%" alt="Xiaomi-MiMo" />
	</picture>
	</div>

	<br/>

	<div align="center" style="line-height: 1;">
	\|
	<a href="https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL" target="_blank">🤗 HuggingFace</a>
	\|
	<a href="https://mimo.xiaomi.com/mimo-v2-6" target="_blank">📰 Blog </a>
	\|
	<a href="https://platform.xiaomimimo.com" target="_blank">🎨 Xiaomi MiMo API Platform </a>
	\|
	<a href="https://aistudio.xiaomimimo.com" target="_blank">🗨️ Xiaomi MiMo Studio </a>
	\|
	<a href="https://mimo.xiaomimimo.com/desktop/" target="_blank">💻 Xiaomi MiMo Desktop </a>
	\|
	</div>

	<br/>

	<div align="center" style="line-height: 1.2;">
	<strong>Community</strong><br/>
	<a href="https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro/blob/main/assets/wechat.jpg" target="_blank">WeChat Group</a>
	\|
	<a href="https://discord.gg/kKC2kNnQEX" target="_blank">Discord</a>
	\|
	<a href="https://t.me/+3T-I0pekOVIyNDBl" target="_blank">Telegram</a>
	\|
	<a href="https://www.reddit.com/r/XiaomiMiMo_Official/" target="_blank">Reddit</a>
	</div>

	<br/>

	# MiMo-V2.6-Flash-RL

	Scaling Reinforcement Learning Toward Self-Improvement

	<p align="center">
	<a href="https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL/blob/main/MiMo_V2_6_technical_report.pdf"><b>Technical Report</b></a>
	</p>

	## 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 — omni encoders, hybrid SWA backbone, and MTP blocks](assets/architecture.png)

	Figure 1. MiMo-V2.6 architecture.

	## 2. Downloads

	\| Model \| Download \|
	\| --- \| --- \|
	\| MiMo-V2.6-Pro-RL \| [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Pro-RL) · 🤖 ModelScope (at release) \|
	\| MiMo-V2.6-Flash-RL \| [🤗 HuggingFace](https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL) · 🤖 ModelScope (at release) \|

	## 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](https://docs.sglang.io/cookbook/autoregressive/Xiaomi/MiMo-V2.5). Docker image: `lmsysorg/sglang:latest`.

	### SGLang

	```bash
	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](https://recipes.vllm.ai/XiaomiMiMo/MiMo-V2.5). Stable vLLM may lag; pre-built image: `docker pull vllm/vllm-openai:mimov25-cu129`.

	```bash
	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

	```bibtex
	@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](mailto:mimo@xiaomi.com) or join our community:

	- [WeChat Group](https://work.weixin.qq.com/apph5/external_room/join/group_mng?plg_id=c417f99bd9014b5dd894daa8bfe19790&)
	- [Discord](https://discord.gg/WX2R2uNp)
	- [Telegram](https://t.me/+3T-I0pekOVIyNDBl)
	- [Reddit](https://www.reddit.com/r/XiaomiMiMo_Official/)