Instructions to use Jiunsong/supergemma4-e4b-abliterated-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jiunsong/supergemma4-e4b-abliterated-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("Jiunsong/supergemma4-e4b-abliterated-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 Jiunsong/supergemma4-e4b-abliterated-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 "Jiunsong/supergemma4-e4b-abliterated-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": "Jiunsong/supergemma4-e4b-abliterated-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jiunsong/supergemma4-e4b-abliterated-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 "Jiunsong/supergemma4-e4b-abliterated-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 Jiunsong/supergemma4-e4b-abliterated-mlx

Run Hermes

hermes

MLX LM

How to use Jiunsong/supergemma4-e4b-abliterated-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 "Jiunsong/supergemma4-e4b-abliterated-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Jiunsong/supergemma4-e4b-abliterated-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Jiunsong/supergemma4-e4b-abliterated-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

SuperGemma4 E4B Abliterated MLX

This is the private Apple Silicon deployment build of supergemma4-e4b-abliterated, converted to MLX and quantized to a compact 4-bit format for fast local use on Mac Studio class hardware.

The original upstream checkpoint is google/gemma-4-E4B-it. This MLX package is the Apple Silicon deployment build of the final abliterated and tuned SuperGemma release derived from that Google E4B base.

If you want the strongest consumer-facing experience in this project line on Apple Silicon, this is the branch to pull first.

What You Get

MLX-native 4-bit packaging
compact single-file weight layout
chat template preserved
strong structured-output behavior inherited from the release candidate
convenient path for local serving and Mac-based agent stacks

Derived From

original upstream base: google/gemma-4-E4B-it
source release: Jiunsong/supergemma4-e4b-abliterated

Release Highlights

The source release backing this MLX build achieved:

release-quality score: 92.34
exact-eval score: 98.50
JSON exact-match: 100%
tool-call accuracy: 90%
exact code score: 100%
exact bug-fix score: 100%
long-context sanity: 100%

Serving and stability validation on the source candidate:

direct reliability audit: 14/14
repeat reliability probe: 90/90
batched soak test: 12/12
simple soak test: 6/6

Target Hardware

Mac Studio
Apple Silicon laptops and desktops
MLX / vMLX local inference setups

Quick Start

from mlx_lm import load, generate

model, tokenizer = load("Jiunsong/supergemma4-e4b-abliterated-mlx")

messages = [
    {"role": "user", "content": "Write valid JSON with keys model and strength."}
]
prompt = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=False,
)

response = generate(model, tokenizer, prompt=prompt, max_tokens=128, verbose=False)
print(response)

Positioning

This branch is for users who want the SuperGemma4 E4B behavior in a lighter, Apple-friendly package that is easy to pull onto a Mac Studio for local testing and agent deployment.