Instructions to use Sciguy429/MiniMax-M3-MSA-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sciguy429/MiniMax-M3-MSA-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Sciguy429/MiniMax-M3-MSA-GGUF",
	filename="IQ2_M_IF16/MiniMax-M3-IQ2_M-IF16_MSA-00001-of-00006.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Sciguy429/MiniMax-M3-MSA-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 Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA
# Run inference directly in the terminal:
llama cli -hf Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA
# Run inference directly in the terminal:
llama cli -hf Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

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 Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA
# Run inference directly in the terminal:
./llama-cli -hf Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

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 Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

Use Docker

docker model run hf.co/Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

LM Studio
Jan
Ollama
How to use Sciguy429/MiniMax-M3-MSA-GGUF with Ollama:
```
ollama run hf.co/Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA
```

Unsloth Studio

How to use Sciguy429/MiniMax-M3-MSA-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Sciguy429/MiniMax-M3-MSA-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Sciguy429/MiniMax-M3-MSA-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Sciguy429/MiniMax-M3-MSA-GGUF to start chatting

How to use Sciguy429/MiniMax-M3-MSA-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Sciguy429/MiniMax-M3-MSA-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 Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

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 Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Sciguy429/MiniMax-M3-MSA-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

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 "Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA" \
  --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"

Docker Model Runner
How to use Sciguy429/MiniMax-M3-MSA-GGUF with Docker Model Runner:
```
docker model run hf.co/Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA
```

Lemonade

How to use Sciguy429/MiniMax-M3-MSA-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sciguy429/MiniMax-M3-MSA-GGUF:F16_MSA

Run and chat with the model

lemonade run user.MiniMax-M3-MSA-GGUF-F16_MSA

List all available models

lemonade list

This is an experimental 2-bit GGUF quant of MiniMax-M3 which supports MSA via this llama.cpp PR: https://github.com/ggml-org/llama.cpp/pull/24908

I will keep these quants (and any more I create) up until the PR merges and the bigger players update there repos to match, then I will likely replace this repo with IK quants once ik_llama catches up.

These are up to date with the PR as of 7-10-2026.

Models:

IF16/IF32?

I am still running tests on this at the moment, but for now. The indexer tensors within the official MiniMax-M3 release are stored as both FP32 and BF16 tensors. Specifically the projection tensors (index_{q,k}_proj) are what use BF16. There is some conflicting information in the PR about what these values should be quantized too. The main description recommends F32, but an example llama-quantize command further down in the chain just uses F16. ~~(Once I get some proper perp testing done I will update this section with any differences I noted. I will leave both model's up regardless!)~~

Further testing has revealed that there is likely no real discernible difference between the F16 and F32 versions of the quant. They have identical KLs and both behave the same in my own usage testing. For now I would recommend the use of the UD-IQ2_M_IF16 model, as it is slightly smaller for what is likely no loss in quality.

UD-IQ2_M_IF16/IF32:

These are clones of Unsloth's UD-IQ2_M mix, with FP16 and FP32 indexer tensors respectively. They behave fairly well compared to the original quant from Unsloth and are likely the highest 'quality' 2-bit quant you can get right now until they update their mix once PR #24098 lands or ik_llama implements it's own support and allows for better 2-bit SOTA quant types.

Mean KLD: 0.488235 ± 0.002577

IQ2_M_IF16:

This is a flat automatic IQ2_M quant directly from llama-quantize without any tensor overrides. I just made this one to compare against the UD quant. NOT RECOMENDED FOR ACTUALL USE!

Mean KLD: 0.490047 ± 0.002621

Downloads last month: 1,476

GGUF

Model size

426B params

Architecture

minimax-m3

Hardware compatibility

2-bit

16-bit

32-bit

View +1 variant

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