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 5 days ago

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@@ -63,10 +63,18 @@ Official GGUF quantisations of **MiMo-V2.6-Flash-REAP50**, a 50% routed-expert p
 ## Running with llama.cpp
-### 1. Standard Text Inference
 ```bash
 ./llama-cli \
-    -m MiMo-V2.6-Flash-REAP50-Q4_K_M.gguf \
     -p "You are MiMo, an AI assistant developed by Xiaomi. Explain how MoE expert pruning works:" \
     -n 512 --temp 0.6
 ```
@@ -74,7 +82,7 @@ Official GGUF quantisations of **MiMo-V2.6-Flash-REAP50**, a 50% routed-expert p
 ### 2. Speculative Decoding with MTP Draft Head
 ```bash
 ./llama-cli \
-    -m MiMo-V2.6-Flash-REAP50-Q4_K_M.gguf \
     --draft-model mtp-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
     -p "Explain quantum teleportation in detail:" \
     -n 512
@@ -83,7 +91,7 @@ Official GGUF quantisations of **MiMo-V2.6-Flash-REAP50**, a 50% routed-expert p
 ### 3. Multimodal Inference (Vision & Audio)
 ```bash
 ./llama-cli \
-    -m MiMo-V2.6-Flash-REAP50-Q4_K_M.gguf \
     --mmproj mmproj-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
     --image input.jpg \
     -p "Describe the contents of this image in detail."
@@ -92,7 +100,7 @@ Official GGUF quantisations of **MiMo-V2.6-Flash-REAP50**, a 50% routed-expert p
 ### 4. OpenAI-Compatible API Server
 ```bash
 ./llama-server \
-    -m MiMo-V2.6-Flash-REAP50-Q4_K_M.gguf \
     --mmproj mmproj-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
     --port 8080 \
     -ngl 99

 ## Running with llama.cpp
+### 1. Standard Text Inference (Optimal Hybrid Q2_K)
 ```bash
 ./llama-cli \
+    -m MiMo-V2.6-Flash-REAP50-Q2_K.gguf \
+    -p "You are MiMo, an AI assistant developed by Xiaomi. Explain how MoE expert pruning works:" \
+    -n 512 --temp 0.6
+```
+Or run the flagship bit-for-bit native MXFP4 checkpoint:
+```bash
+./llama-cli \
+    -m MiMo-V2.6-Flash-REAP50-MXFP4_MOE.gguf \
     -p "You are MiMo, an AI assistant developed by Xiaomi. Explain how MoE expert pruning works:" \
     -n 512 --temp 0.6
 ```
 ### 2. Speculative Decoding with MTP Draft Head
 ```bash
 ./llama-cli \
+    -m MiMo-V2.6-Flash-REAP50-Q2_K.gguf \
     --draft-model mtp-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
     -p "Explain quantum teleportation in detail:" \
     -n 512
 ### 3. Multimodal Inference (Vision & Audio)
 ```bash
 ./llama-cli \
+    -m MiMo-V2.6-Flash-REAP50-Q2_K.gguf \
     --mmproj mmproj-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
     --image input.jpg \
     -p "Describe the contents of this image in detail."
 ### 4. OpenAI-Compatible API Server
 ```bash
 ./llama-server \
+    -m MiMo-V2.6-Flash-REAP50-Q2_K.gguf \
     --mmproj mmproj-MiMo-V2.6-Flash-REAP50-Q8_0.gguf \
     --port 8080 \
     -ngl 99