Instructions to use kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-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 kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-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 kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

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 kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

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 kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

Use Docker

docker model run hf.co/kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF with Ollama:
```
ollama run hf.co/kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0
```
Unsloth Desktop

How to use kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

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": "kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0
```

Lemonade

How to use kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

Run and chat with the model

lemonade run user.MiMo-V2.6-Flash-MOPD-MXFP4-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-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 kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

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 kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0

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 "kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF:Q8_0" \
  --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"

MiMo-V2.6 Flash MOPD GGUF for DwarfStar

GGUF files for the MiMo-V2.6 Flash port in DwarfStar (DS4), branch kernelpool/ds4:mimo-v26 (pull request: TODO link). Metal only.

MOPD is Xiaomi's update of the MiMo-V2.6 Flash RL checkpoint that reduces repeated tool calls in agent sessions (Xiaomi's write-up). Only the main model changed: the MTP blocks, the DFlash drafter and the vision tower carry the same weights as the RL release. The files below replace kernelpool/MiMo-V2.6-Flash-MXFP4-GGUF and use the same layout. Requantized Q4 and Q2 files of the same checkpoint are in kernelpool/MiMo-V2.6-Flash-MOPD-GGUF.

File	Size	Content
`MiMo-V2.6-Flash-MOPD-MXFP4.gguf`	157.4 GiB	Main model, `mimo2` layout: the checkpoint's own MXFP4 experts repacked without requantization, Q8_0 attention, dense and output weights, BF16 embeddings, the three MTP blocks
`MiMo-V2.6-Flash-MOPD-DFlash-Q8_0.gguf`	1.5 GiB	DFlash drafter sidecar, `dflash` layout plus the mask embedding and value scale DS4 needs
`MiMo-V2.6-Flash-MOPD-Vision-F32.gguf`	2.7 GiB	Vision encoder, lossless (recommended)
`MiMo-V2.6-Flash-MOPD-Vision-Q8_0.gguf`	0.7 GiB	Vision encoder, Q8_0 matrices, smaller and measurably less exact

Run

git clone -b mimo-v26 https://github.com/kernelpool/ds4 && cd ds4 && make
./ds4 -m MiMo-V2.6-Flash-MOPD-MXFP4.gguf --mtp
./ds4 -m MiMo-V2.6-Flash-MOPD-MXFP4.gguf --mtp --vision MiMo-V2.6-Flash-MOPD-Vision-F32.gguf
./ds4-server -m MiMo-V2.6-Flash-MOPD-MXFP4.gguf --mtp --vision MiMo-V2.6-Flash-MOPD-Vision-F32.gguf

--mtp drafts with the MTP blocks inside the main file and is the faster option; --mtp-model MiMo-V2.6-Flash-MOPD-DFlash-Q8_0.gguf uses the DFlash sidecar instead. Both verify against the target, so temperature-zero output follows plain decoding. The main file needs a 192 GB or larger Mac. See docs/MIMO_V26.md.

Conversion

Written by gguf-tools/mimo26_quantize.py and gguf-tools/mimo26_vision.py from XiaomiMiMo/MiMo-V2.6-Flash-MOPD at revision 2479e2d0029eca9a34cc7e7f55a121925f81908e. The converter de-interleaves the tensor-parallel chunks of the fused QKV projection and keeps the released MXFP4 expert blocks bit for bit; the source revision is recorded in the GGUF metadata.

Quality

Scored on 100 official continuations from the Xiaomi platform with the fixture in gguf-tools/quality-testing/mimo-v2.6-flash-20260922; the MOPD file scores on par with the RL release there. The vision encoder reproduces the checkpoint's tower on identical inputs.

The original checkpoint is released by Xiaomi under the MIT license, which applies to these files as well.

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GGUF

Model size

310B params

Architecture

mimo2

Hardware compatibility

4-bit

8-bit

32-bit

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