Instructions to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS
# Run inference directly in the terminal:
./llama-cli -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Use Docker

docker model run hf.co/webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

LM Studio
Jan

vLLM

How to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Ollama
How to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with Ollama:
```
ollama run hf.co/webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS
```
Unsloth Desktop

How to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with Docker Model Runner:
```
docker model run hf.co/webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS
```

Lemonade

How to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Run and chat with the model

lemonade run user.Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF-IQ2_XS

List all available models

lemonade list

Hermes Agent

How to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "webmp3/Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF:IQ2_XS" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Sakura-MiMo-V2.6-Flash-MOPD-P160-GGUF / NOTICE.md

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NOTICE — sources and credits

This is an independent community build. It is not an official Xiaomi release.

Component	Source	License
Base model	XiaomiMiMo/MiMo-V2.6-Flash-MOPD	MIT
Importance matrix (computed on MiMo-V2.6-Flash-RL, routers identical), cut to the kept experts	Baekpica/MiMo-V2.6-Flash-RL-Mixed-Quant-GGUF	MIT (calibration data under their own terms, see that repo)
Expert-usage reference for the selection (full RL model)	TrevorJS/MiMo-V2.6-Flash-RL-GGUF v2.1 (used only for measurements)	MIT
REAP saliency bands (REAP25 ⊂ REAP37 ⊂ REAP50 pruned-expert lists)	tacodevs MiMo-V2.6-Flash-RL MLX REAP releases (`compression_alloc.json`)	MIT
Decode-trace hot-expert list	SSHdotCodes/mimo-v2.6-flash-pro6000 (`profiles/hot-experts.txt`)	MIT
REAP method	CerebrasResearch/reap, arXiv:2510.13999	Apache-2.0 (method only, no code used)
Runtime	ggml-org/llama.cpp + ek15072809/Swap-MoE (SSD expert streaming)	MIT

Own contributions: routing measurements of the full model (German text, code, and the model's own on-policy German answers), the combined expert-selection rule, the ranged downloader/converter from the original weights, the quantization recipe, the runtime patches in runtime/patches (expert lock, UTF-8 sanitizing, DFlash for MiMo, Swap-MoE GPU guard) and all evaluations reported in the model card.