Instructions to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-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 patrickbdevaney/MiMo-V2.6-Flash-REAP50-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 patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

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 patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

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 patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

LM Studio
Jan
Ollama
How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Ollama:
```
ollama run hf.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
```
Unsloth Desktop

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

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": "patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Docker Model Runner:
```
docker model run hf.co/patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE
```

Lemonade

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.MiMo-V2.6-Flash-REAP50-GGUF-MXFP4_MOE

List all available models

lemonade list

Hermes Agent

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-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 patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

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 patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE

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 "patrickbdevaney/MiMo-V2.6-Flash-REAP50-GGUF:MXFP4_MOE" \
  --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"

patrickbdevaney commited on 6 days ago

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@@ -32,7 +32,7 @@ Official GGUF quantisations of **MiMo-V2.6-Flash-REAP50**, a 50% routed-expert p
 | Filename | Quant Type | Size | Description | Recommended VRAM / RAM |
 | :--- | :--- | :--- | :--- | :--- |
 | `MiMo-V2.6-Flash-REAP50-MXFP4_MOE.gguf` | **MXFP4_MOE** | 86.06 GiB | Flagship: 1-to-1 native packed MXFP4 experts (32 blk) + BF16 attention/trunk. Exact bit-level fidelity to REAP base. | 96 GiB+ / 1x 128GB Thor or 2x 48GB |
-| `MiMo-V2.6-Flash-REAP50-Q2_K.gguf` | **Q2_K** | Pending | Optimal Hybrid MoE: sensitive down-projections kept in native MXFP4, gate/up in Q2_K, trunk in Q8_0 (~3.36 BPW). | 64 GiB+ / 3x 24GB GPUs (72GB) or Mac 64-96GB |
 ### Supporting Towers (Vision, Audio & MTP)

 | Filename | Quant Type | Size | Description | Recommended VRAM / RAM |
 | :--- | :--- | :--- | :--- | :--- |
 | `MiMo-V2.6-Flash-REAP50-MXFP4_MOE.gguf` | **MXFP4_MOE** | 86.06 GiB | Flagship: 1-to-1 native packed MXFP4 experts (32 blk) + BF16 attention/trunk. Exact bit-level fidelity to REAP base. | 96 GiB+ / 1x 128GB Thor or 2x 48GB |
+| `MiMo-V2.6-Flash-REAP50-Q2_K.gguf` | **Q2_K** | 61.64 GiB | Optimal Hybrid MoE: sensitive down-projections kept in native MXFP4, gate/up in Q2_K, trunk in Q8_0 (~3.36 BPW). | 64 GiB+ / 3x 24GB GPUs (72GB) or Mac 64-96GB |
 ### Supporting Towers (Vision, Audio & MTP)