Instructions to use BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-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 BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-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 BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

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 BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

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 BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-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": "BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

Ollama
How to use BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF with Ollama:
```
ollama run hf.co/BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

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": "BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF with Docker Model Runner:
```
docker model run hf.co/BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M
```

Lemonade

How to use BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniMax-M2.5-REAP-139B-A10B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-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 BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

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 BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M

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 "BennyDaBall/MiniMax-M2.5-REAP-139B-A10B-GGUF:Q4_K_M" \
  --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"

BennyDaBall commited on Feb 19

Commit

d755148

verified ·

1 Parent(s): c182c22

Add model card

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README.md +58 -0

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	@@ -0,0 +1,58 @@

+---
+license: other
+base_model:
+- MiniMaxAI/MiniMax-M2.5
+language:
+- en
+tags:
+- gguf
+- minimax
+- moe
+- reap
+- text-generation
+pipeline_tag: text-generation
+---
+# MiniMax-M2.5-REAP-139B-A10B-GGUF
+This is the REAP model in practical pants: high quality GGUF quants for local inference without setting your workstation on fire.
+Built from:
+- Base: `MiniMaxAI/MiniMax-M2.5`
+- REAP source: `tomngdev/MiniMax-M2.5-REAP-139B-A10B-GGUF` (BF16 split)
+- Quantized locally with `llama.cpp` on Strix Halo + high RAM mode.
+## Available Quants
+| Quant | Status | Size (GiB) | Notes |
+|---|---|---:|---|
+| `Q8_0` | uploaded | 137.78 | Highest quality quant in this pack |
+| `Q5_K_M` | processing/uploading | TBD | Better quality/size balance |
+| `Q4_K_M` | uploaded | 78.83 | Strong practical default |
+## File Layout
+All quants are split GGUF sets (`00001-of-00007` etc.) for safer handling of very large models.
+## Quality Notes
+- These are generated from BF16 REAP GGUF, not requantized from lower precision.
+- Token embedding and output tensors are kept at `Q8_0` during quantization for quality retention.
+## Usage
+Use any first shard with `llama.cpp`; it auto-discovers sibling shards:
+```bash
+llama-cli -m MiniMax-M2.5-REAP-Q4_K_M-00001-of-00007.gguf -ngl 0 -c 8192
+```
+## Credits
+- `MiniMaxAI` for MiniMax-M2.5
+- `tomngdev` for the BF16 REAP GGUF release
+- `BennyDaBall` for this quant pack
+## Disclaimer
+You are responsible for your own use, outputs, and compliance with applicable laws and platform policies.