Instructions to use inferencerlabs/MiniMax-M3-MLX-Q8.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inferencerlabs/MiniMax-M3-MLX-Q8.5 with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("inferencerlabs/MiniMax-M3-MLX-Q8.5")
config = load_config("inferencerlabs/MiniMax-M3-MLX-Q8.5")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use inferencerlabs/MiniMax-M3-MLX-Q8.5 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "inferencerlabs/MiniMax-M3-MLX-Q8.5"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "inferencerlabs/MiniMax-M3-MLX-Q8.5"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use inferencerlabs/MiniMax-M3-MLX-Q8.5 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "inferencerlabs/MiniMax-M3-MLX-Q8.5"

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 inferencerlabs/MiniMax-M3-MLX-Q8.5

Run Hermes

hermes

OpenClaw new

How to use inferencerlabs/MiniMax-M3-MLX-Q8.5 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "inferencerlabs/MiniMax-M3-MLX-Q8.5"

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 "inferencerlabs/MiniMax-M3-MLX-Q8.5" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MiniMax-M3

See MiniMax-M3 in action: demonstration videos

Tested with an M3 Ultra 512 GiB using Inferencer app v2.0.5

Multimodal Inference: ~19 tokens/s @ 1000 tokens ~422 GiB

Q8.5 typically achieves near lossless quality in our coding test.

Quantization (bpw)	Perplexity	Token Accuracy	Missed Divergence
Q4.5	1.35937	89.75%	28.98%
Q5.5	1.24218	94.60%	17.55%
Q6.5	1.21875	96.85%	16.03%
Q8.5	1.21875	97.65%	9.92%
Base	1.20312	100.0%	0.000%

Perplexity: Measures the confidence for predicting base tokens (lower is better).
Token Accuracy: The percentage of correctly generated base tokens.
Missed Divergence: Measures severity of misses; how much the token was missed by.

Quantized with a modified version of MLX.

For more details see our demonstration videos or visit zai-org/GLM-5.1.

Disclaimer

We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.

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