Instructions to use nightmedia/granite-4.1-3b-mxfp8-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/granite-4.1-3b-mxfp8-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-4.1-3b-mxfp8-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 Settings
LM Studio

How to use nightmedia/granite-4.1-3b-mxfp8-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-4.1-3b-mxfp8-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-4.1-3b-mxfp8-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use nightmedia/granite-4.1-3b-mxfp8-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-4.1-3b-mxfp8-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nightmedia/granite-4.1-3b-mxfp8-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-4.1-3b-mxfp8-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use nightmedia/granite-4.1-3b-mxfp8-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-4.1-3b-mxfp8-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-4.1-3b-mxfp8-mlx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nightmedia/granite-4.1-3b-mxfp8-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/granite-4.1-3b-mxfp8-mlx"

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 "nightmedia/granite-4.1-3b-mxfp8-mlx" \
  --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"

granite-4.1-3b-mxfp8-mlx

Brainwaves

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.406,0.581,0.821,0.484,0.434,0.712,0.559

Other models in this size

gemma-4-E2B-it

mxfp8    0.376,0.464,0.743,0.490,0.378,0.709,0.622
mxfp4    0.380,0.451,0.762,0.494,0.374,0.699,0.594
q8-hi    0.392,0.462,0.762,0.487,0.376,0.706,0.636

Qwen3.5-4B

mxfp8    0.392,0.441,0.627,0.601,0.360,0.739,0.590
mxfp4    0.371,0.444,0.632,0.585,0.356,0.732,0.548
q8-hi    0.398,0.435,0.622,0.604,0.362,0.732,0.585
q8       0.398,0.434,0.622,0.604,0.362,0.733,0.582

This model granite-4.1-3b-mxfp8-mlx was converted to MLX format from ibm-granite/granite-4.1-3b using mlx-lm version 0.31.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("granite-4.1-3b-mxfp8-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

3B params

Tensor type

U32

BF16

MLX

Hardware compatibility

8-bit

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