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

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

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

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

Use Docker

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

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

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

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-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.MiMo-V2.6-Flash-MOPD-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

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

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-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

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

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-GGUF" \
  --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 (Q4, Q2) for DwarfStar

Requantized GGUF files for the MiMo-V2.6 Flash port in DwarfStar (DS4), branch kernelpool/ds4:mimo-v26. 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). The checkpoint ships its routed experts in MXFP4; the lossless MXFP4 file, the DFlash drafter and the vision encoder are in kernelpool/MiMo-V2.6-Flash-MOPD-MXFP4-GGUF.

File	Size	Content
`MiMo-V2.6-Flash-MOPD-Q4.gguf`	166.2 GiB	Main model, `mimo2` layout: Q4_K experts (gate, up and down) requantized from the released MXFP4 experts with an importance matrix, Q8_0 attention, dense and output weights, BF16 embeddings, the three MTP blocks
`MiMo-V2.6-Flash-MOPD-Q2.gguf`	86.9 GiB	Main model with IQ2_XXS gate/up and Q2_K down experts, requantized the same way; all other tensors as in the Q4 file
`imatrix/MiMo-V2.6-Flash-MOPD-routed-moe-ds4pool.dat`	0.5 GiB	The routed-expert importance matrix both files were quantized with

Run

git clone -b mimo-v26 https://github.com/kernelpool/ds4 && cd ds4 && make
./ds4 -m MiMo-V2.6-Flash-MOPD-Q2.gguf --mtp
./ds4 -m MiMo-V2.6-Flash-MOPD-Q2.gguf --mtp --vision MiMo-V2.6-Flash-MOPD-Vision-F32.gguf
./ds4-server -m MiMo-V2.6-Flash-MOPD-Q2.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 vision encoder and DFlash files come from the MXFP4 repository linked above and work with both files here. Resident sizes are 166.2 GiB for Q4 and 86.9 GiB for Q2; Q2 is the tier for 128 GB Macs. See docs/MIMO_V26.md.

Conversion

Written by gguf-tools/mimo26_quantize.py (--quant q4 and --quant q2) from XiaomiMiMo/MiMo-V2.6-Flash-MOPD at revision 2479e2d0029eca9a34cc7e7f55a121925f81908e. The experts are requantized from the released MXFP4 blocks using the importance matrix above, which DS4 collected on this checkpoint over its calibration prompts (gguf-tools/imatrix/dataset). 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 Q4 file scores on par with the MXFP4 file, and the Q2 file trades some accuracy for its smaller size.

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