Instructions to use AesSedai/MiniMax-M3-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 AesSedai/MiniMax-M3-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 AesSedai/MiniMax-M3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AesSedai/MiniMax-M3-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 AesSedai/MiniMax-M3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AesSedai/MiniMax-M3-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 AesSedai/MiniMax-M3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AesSedai/MiniMax-M3-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 AesSedai/MiniMax-M3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AesSedai/MiniMax-M3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/AesSedai/MiniMax-M3-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use AesSedai/MiniMax-M3-GGUF with Ollama:
```
ollama run hf.co/AesSedai/MiniMax-M3-GGUF:Q4_K_M
```
Unsloth Desktop

How to use AesSedai/MiniMax-M3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AesSedai/MiniMax-M3-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": "AesSedai/MiniMax-M3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AesSedai/MiniMax-M3-GGUF with Docker Model Runner:
```
docker model run hf.co/AesSedai/MiniMax-M3-GGUF:Q4_K_M
```

Lemonade

How to use AesSedai/MiniMax-M3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AesSedai/MiniMax-M3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniMax-M3-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AesSedai/MiniMax-M3-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 AesSedai/MiniMax-M3-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 AesSedai/MiniMax-M3-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AesSedai/MiniMax-M3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AesSedai/MiniMax-M3-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 "AesSedai/MiniMax-M3-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"

This repo contains specialized MoE-quants for MiniMaxAI/MiniMax-M3. The idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization. To that end, the quantization type default is kept in high quality and the FFN UP + FFN GATE tensors are quanted down along with the FFN DOWN tensors.

Additionally, I've provided a J-Space lens in the lens/ folder, trained on ~128 text prompts.

Quant	Size	Mixture	PPL	1-(Mean PPL(Q)/PPL(base))	KLD
Q8_0	421.84 GiB (8.50 BPW)	Q8_0	5.202737 ± 0.034827	+0.3357%	0.030136 ± 0.000884
Q5_K_M	295.20 GiB (5.95 BPW)	Q8_0 / Q5_K / Q5_K / Q6_K	5.218907 ± 0.034969	+0.6475%	0.041986 ± 0.001016
Q4_K_M	246.11 GiB (4.96 BPW)	Q8_0 / Q4_K / Q4_K / Q5_K	5.282848 ± 0.035408	+1.8807%	0.068863 ± 0.000982
IQ4_XS	189.12 GiB (3.81 BPW)	Q6_K / IQ3_S / IQ3_S / IQ4_XS	5.657391 ± 0.038606	+9.1038%	0.151112 ± 0.001437
IQ3_S	148.04 GiB (2.98 BPW)	Q6_K / IQ2_S / IQ2_S / IQ3_S	6.417468 ± 0.044920	+23.7620%	0.321256 ± 0.002052
IQ2_S	134.01 GiB (2.70 BPW)	Q6_K / IQ2_XS / IQ2_XS / IQ3_XXS	6.636852 ± 0.046125	+27.9929%	0.397892 ± 0.002336