Instructions to use spicyneuron/MiniMax-M2.7-MLX-4.9bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/MiniMax-M2.7-MLX-4.9bit with MLX:

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

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("spicyneuron/MiniMax-M2.7-MLX-4.9bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

Pi new

How to use spicyneuron/MiniMax-M2.7-MLX-4.9bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/MiniMax-M2.7-MLX-4.9bit"

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": "spicyneuron/MiniMax-M2.7-MLX-4.9bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/MiniMax-M2.7-MLX-4.9bit 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 "spicyneuron/MiniMax-M2.7-MLX-4.9bit"

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 spicyneuron/MiniMax-M2.7-MLX-4.9bit

Run Hermes

hermes

MLX LM

How to use spicyneuron/MiniMax-M2.7-MLX-4.9bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "spicyneuron/MiniMax-M2.7-MLX-4.9bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "spicyneuron/MiniMax-M2.7-MLX-4.9bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "spicyneuron/MiniMax-M2.7-MLX-4.9bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

MiniMax-M2.7-MLX-4.9bit / README.md

spicyneuron

Update README.md

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pipeline_tag: text-generation
license: other
license_name: modified-mit
license_link: https://github.com/MiniMax-AI/MiniMax-M2.7/blob/main/LICENSE
library_name: mlx
tags:
  - mlx
base_model: MiniMaxAI/MiniMax-M2.7

MiniMax-M2.7 optimized for MLX.

A mixed-precision quant that balances speed, memory, and accuracy.
4 bit baseline with important layers at 5, 6, 8, and BF16.

Usage

# Start server at http://localhost:8080/chat/completions
uvx --from mlx-lm mlx_lm.server \
  --host 127.0.0.1 \
  --port 8080 \
  --model spicyneuron/MiniMax-M2.7-MLX-4.9bit

Benchmarks

metric	mlx-community_MiniMax-M2.7-4bit	baa-ai_MiniMax-M2.7-RAM-155GB-MLX	4.9 bit (this model)
bpw	4.501	5.4278	4.915
peak memory (1024/512)	129.632	156.051	141.458
prompt tok/s (1024)	739.996 ± 1.565	708.147 ± 0.818	723.742 ± 0.880
gen tok/s (512)	48.703 ± 0.116	40.253 ± 0.077	42.270 ± 0.143
perplexity	9.120 ± 0.047	8.835 ± 0.045	4.590 ± 0.027
hellaswag	0.504 ± 0.011	0.509 ± 0.011	0.512 ± 0.011
piqa	0.786 ± 0.01	0.787 ± 0.01	0.791 ± 0.009
winogrande	0.636 ± 0.014	0.661 ± 0.013	0.666 ± 0.013

Tested on a Mac Studio M3 Ultra with:

mlx_lm.perplexity --sequence-length 2048 --seed 123
mlx_lm.benchmark --prompt-tokens 1024 --generation-tokens 512 --num-trials 5
mlx_lm.evaluate --tasks hellaswag --seed 123 --num-shots 0 --limit 2000
mlx_lm.evaluate --tasks piqa --seed 123 --num-shots 0 --limit 2000
mlx_lm.evaluate --tasks winogrande --seed 123 --num-shots 0 --limit 2000

Methodology

Quantized with a mlx-lm fork, drawing inspiration from Unsloth/AesSedai/ubergarm style mixed-precision GGUFs. MLX quantization options differ than llama.cpp, but the principles are the same:

Sensitive layers like MoE routing, attention, and output embeddings get higher precision
More tolerant layers like MoE experts get lower precision