Instructions to use nightmedia/granite-4.1-3b-Abliterated-AND-Disinhibited-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-Abliterated-AND-Disinhibited-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-Abliterated-AND-Disinhibited-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-Abliterated-AND-Disinhibited-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-Abliterated-AND-Disinhibited-mxfp8-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-4.1-3b-Abliterated-AND-Disinhibited-mxfp8-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

MLX LM

How to use nightmedia/granite-4.1-3b-Abliterated-AND-Disinhibited-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-Abliterated-AND-Disinhibited-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-Abliterated-AND-Disinhibited-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-Abliterated-AND-Disinhibited-mxfp8-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

granite-4.1-3b-Abliterated-AND-Disinhibited-mxfp8-mlx / README.md

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metadata

license: apache-2.0
library_name: mlx
base_model: treadon/granite-4.1-3b-Abliterated-AND-Disinhibited
tags:
  - abliteration
  - disinhibition
  - granite
  - mechanistic-interpretability
  - mlx
pipeline_tag: text-generation

granite-4.1-3b-Abliterated-AND-Disinhibited-mxfp8-mlx

Brainwaves

         arc   arc/e boolq hswag obkqa piqa  wino
mxfp8    0.405,0.598,0.843,0.520,0.442,0.713,0.582

Quant    Perplexity      Peak Memory   Tokens/sec
mxfp8   11.595 ± 0.129   6.58 GB       1594

Original model

granite-4.1-3b
mxfp8    0.406,0.581,0.821,0.484,0.434,0.712,0.559

Quant    Perplexity      Peak Memory   Tokens/sec
mxfp8   11.346 ± 0.127   6.58 GB       1690

This model granite-4.1-3b-Abliterated-AND-Disinhibited-mxfp8-mlx was converted to MLX format from treadon/granite-4.1-3b-Abliterated-AND-Disinhibited 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-Abliterated-AND-Disinhibited-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)