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

Use Docker

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

LM Studio
Jan

vLLM

How to use bartowski/MiniMax-M3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/MiniMax-M3-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": "bartowski/MiniMax-M3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

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

Ollama
How to use bartowski/MiniMax-M3-GGUF with Ollama:
```
ollama run hf.co/bartowski/MiniMax-M3-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/MiniMax-M3-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 bartowski/MiniMax-M3-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 bartowski/MiniMax-M3-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

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

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": "bartowski/MiniMax-M3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use bartowski/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 bartowski/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 "bartowski/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"

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/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

MiniMax-M3-GGUF / README.md

bartowski

Update README.md

2ee48e5 verified 23 minutes ago

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	---
	quantized_by: bartowski
	pipeline_tag: image-text-to-text
	license_link: LICENSE
	tags:
	- multimodal
	- moe
	- agent
	- coding
	- video
	base_model_relation: quantized
	license: other
	license_name: minimax-community
	base_model: MiniMaxAI/MiniMax-M3
	---

	## Llamacpp imatrix Quantizations of MiniMax-M3 by MiniMaxAI

	Using <a href="https://github.com/ggml-org/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b10141">b10141</a> for quantization.

	Original model: https://huggingface.co/MiniMaxAI/MiniMax-M3

	Model details:
	- Parameter count: 427B
	- Input support: text, image (with mmproj file) - [details](#multimodal)
	- MTP: no
	- imatrix: yes - [details](#imatrix)

	[How to run](#how-to-run)

	## Prompt format

	```
	]~!b[]~b]system
	Your model version is MiniMax-M3, developed by MiniMax. Knowledge cutoff: January 2026. Founded in early 2022, MiniMax is a global AI foundation model company committed to advancing the frontiers of AI towards AGI.

	<thinking_instructions>
	You have a thinking capability that allows you to reason step by step before responding. When thinking is enabled, wrap your reasoning in <mm:think></mm:think> tags before your response. When thinking is disabled, begin your response directly after the </mm:think> prefix. When thinking is adaptive, decide on your own whether to think for the current turn.
	Current thinking mode: adaptive. You are encouraged to think for complex decision-making, multi-step reasoning, or when analyzing function/tool results.
	</thinking_instructions>[e~[
	]~b]developer
	{system_prompt}[e~[
	]~b]user
	{prompt}[e~[
	]~b]ai
	```

	Don't know which to choose? Grab [Q4_K_M](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q4_K_M) (261.28GB) - usually a good mix of size and performance. Download instructions available [here](#downloading-using-the-hugging-face-cli)

	## Available files:

	\| Filename \| Quant type \| File Size \| Split \| Description \|
	\| -------- \| ---------- \| --------- \| ----- \| ----------- \|
	\| [MiniMax-M3-Q8_0.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q8_0) \| Q8_0 \| 453.61GB \| true \| Extremely high quality, generally unneeded but max available quant. \|
	\| [MiniMax-M3-Q6_K.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q6_K) \| Q6_K \| 369.40GB \| true \| Very high quality, near perfect, recommended. \|
	\| [MiniMax-M3-Q5_K_M.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q5_K_M) \| Q5_K_M \| 305.35GB \| true \| High quality, recommended. \|
	\| [MiniMax-M3-Q5_K_S.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q5_K_S) \| Q5_K_S \| 295.23GB \| true \| High quality, recommended. \|
	\| [MiniMax-M3-Q4_1.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q4_1) \| Q4_1 \| 268.89GB \| true \| Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. \|
	\| [MiniMax-M3-Q4_K_M.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q4_K_M) \| Q4_K_M \| 261.28GB \| true \| Good quality, default size for most use cases, recommended. \|
	\| [MiniMax-M3-Q4_K_S.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q4_K_S) \| Q4_K_S \| 251.36GB \| true \| Slightly lower quality with more space savings, recommended. \|
	\| [MiniMax-M3-Q4_0.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q4_0) \| Q4_0 \| 243.64GB \| true \| Legacy format, kept for compatibility with older tools. \|
	\| [MiniMax-M3-IQ4_NL.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ4_NL) \| IQ4_NL \| 242.75GB \| true \| Similar to IQ4_XS, but slightly larger. \|
	\| [MiniMax-M3-IQ4_XS.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ4_XS) \| IQ4_XS \| 229.69GB \| true \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \|
	\| [MiniMax-M3-Q3_K_XL.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q3_K_XL) \| Q3_K_XL \| 206.05GB \| true \| Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. \|
	\| [MiniMax-M3-IQ3_M.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ3_M) \| IQ3_M \| 205.52GB \| true \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \|
	\| [MiniMax-M3-Q3_K_L.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q3_K_L) \| Q3_K_L \| 204.97GB \| true \| Lower quality but usable, good for low RAM availability. \|
	\| [MiniMax-M3-Q3_K_M.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q3_K_M) \| Q3_K_M \| 196.61GB \| true \| Low quality. \|
	\| [MiniMax-M3-IQ3_XS.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ3_XS) \| IQ3_XS \| 196.36GB \| true \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \|
	\| [MiniMax-M3-Q3_K_S.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q3_K_S) \| Q3_K_S \| 187.25GB \| true \| Low quality, not recommended. \|
	\| [MiniMax-M3-IQ3_XXS.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ3_XXS) \| IQ3_XXS \| 180.01GB \| true \| Lower quality, new method with decent performance, comparable to Q3 quants. \|
	\| [MiniMax-M3-Q2_K_L.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q2_K_L) \| Q2_K_L \| 153.09GB \| true \| Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. \|
	\| [MiniMax-M3-Q2_K.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-Q2_K) \| Q2_K \| 151.89GB \| true \| Very low quality but surprisingly usable. \|
	\| [MiniMax-M3-IQ2_M.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ2_M) \| IQ2_M \| 145.58GB \| true \| Relatively low quality, uses SOTA techniques to be surprisingly usable. \|
	\| [MiniMax-M3-IQ2_S.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ2_S) \| IQ2_S \| 132.14GB \| true \| Low quality, uses SOTA techniques to be usable. \|
	\| [MiniMax-M3-IQ2_XS.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ2_XS) \| IQ2_XS \| 129.52GB \| true \| Low quality, uses SOTA techniques to be usable. \|
	\| [MiniMax-M3-IQ2_XXS.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ2_XXS) \| IQ2_XXS \| 116.61GB \| true \| Very low quality, uses SOTA techniques to be usable. \|
	\| [MiniMax-M3-IQ1_M.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ1_M) \| IQ1_M \| 100.74GB \| true \| Extremely low quality, not recommended. \|
	\| [MiniMax-M3-IQ1_S.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/tree/main/MiniMax-M3-IQ1_S) \| IQ1_S \| 90.53GB \| true \| Extremely low quality, not recommended. \|

	## Downloading using the Hugging Face CLI

	<details>
	<summary>Click to view download instructions</summary>

	First, make sure you have the Hugging Face CLI installed:

	```
	pip install -U "huggingface_hub[cli]"
	```

	The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:

	```
	hf download bartowski/MiniMax-M3-GGUF --include "MiniMax-M3-Q8_0/*" --local-dir ./
	```

	You can either specify a new local-dir (MiniMax-M3-Q8_0) or download them all in place (./)

	</details>

	## How to run

	These quants run with [llama.cpp](https://github.com/ggml-org/llama.cpp) - installable in one line via [llama.app](https://llama.app/):

	```
	curl -LsSf https://llama.app/install.sh \| sh
	llama-server -hf bartowski/MiniMax-M3-GGUF:Q4_K_M
	```

	llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.

	These quants were made with llama.cpp release b10141 - if this model's architecture is newly supported, you'll need that release or newer to run them.

	They also work in: [LM Studio](https://lmstudio.ai/) · [koboldcpp](https://github.com/LostRuins/koboldcpp) · [ramalama](https://github.com/containers/ramalama) · [Jan AI](https://www.jan.ai/) · [Text Generation Web UI](https://github.com/oobabooga/text-generation-webui) · [LoLLMs](https://github.com/ParisNeo/lollms) · [Atomic Chat](https://atomic.chat/)

	## Multimodal

	This model supports multimodal input. Alongside the quants, this repo includes the multimodal projector files [mmproj-MiniMax-M3-f16.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/blob/main/mmproj-MiniMax-M3-f16.gguf) and [mmproj-MiniMax-M3-bf16.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/blob/main/mmproj-MiniMax-M3-bf16.gguf), which pair with any quant above.

	llama.cpp downloads the mmproj automatically when using `-hf` as shown above; if you're loading files manually, pass it with `--mmproj`.

	## imatrix

	All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/82ae9b520227f57d79ba04add13d0d0d). The imatrix is available here: [MiniMax-M3-imatrix.gguf](https://huggingface.co/bartowski/MiniMax-M3-GGUF/blob/main/MiniMax-M3-imatrix.gguf).

	## Embed/output weights

	Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

	## ARM/AVX information

	llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in [this PR](https://github.com/ggml-org/llama.cpp/pull/9921). This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.

	## Which file should I choose?

	<details>
	<summary>Click here for details</summary>

	An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)

	The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

	If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

	If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

	Hugging Face can also do this math for you: add your hardware in your [Local Apps settings](https://huggingface.co/settings/local-apps) and the model page will show which files fit.

	Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

	If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

	If you want to get more into the weeds, you can check out this extremely useful feature chart:

	[llama.cpp feature matrix](https://github.com/ggml-org/llama.cpp/wiki/Feature-matrix)

	But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

	These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

	</details>

	## Credits

	Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

	Thank you ZeroWw for the inspiration to experiment with embed/output.

	Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski