Instructions to use nightmedia/granite-4.1-30b-mxfp4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/granite-4.1-30b-mxfp4-mlx 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("nightmedia/granite-4.1-30b-mxfp4-mlx")

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 nightmedia/granite-4.1-30b-mxfp4-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/granite-4.1-30b-mxfp4-mlx"

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": "nightmedia/granite-4.1-30b-mxfp4-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/granite-4.1-30b-mxfp4-mlx 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 "nightmedia/granite-4.1-30b-mxfp4-mlx"

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 nightmedia/granite-4.1-30b-mxfp4-mlx

Run Hermes

hermes

MLX LM

How to use nightmedia/granite-4.1-30b-mxfp4-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "nightmedia/granite-4.1-30b-mxfp4-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nightmedia/granite-4.1-30b-mxfp4-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "nightmedia/granite-4.1-30b-mxfp4-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

granite-4.1-30b-mxfp4-mlx

Brainwaves

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.456,0.572,0.897,0.621,0.444,0.757,0.616
mxfp4    0.453,0.565,0.892,0.624,0.442,0.759,0.585
qx86-hi  0.451,0.568,0.897,0.636,0.440,0.763,0.598
qx64-hi  0.462,0.582,0.896,0.642,0.448,0.769,0.600

Quant    Perplexity      Peak Memory   Tokens/sec
mxfp8   10.150 ± 0.120   33.36 GB      188
mxfp4   10.353 ± 0.119   18.93 GB      164
qx86-hi 10.040 ± 0.121   32.24 GB      158

This model granite-4.1-30b-mxfp4-mlx was converted to MLX format from ibm-granite/granite-4.1-30b using mlx-lm version 0.31.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("granite-4.1-30b-mxfp4-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

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

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Safetensors

Model size

29B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for nightmedia/granite-4.1-30b-mxfp4-mlx

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ibm-granite/granite-4.1-30b

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