license: gemma library_name: mlx base_model:

google/gemma-4-E4B-it
Jiunsong/supergemma4-e4b-abliterated tags:
gemma
mlx
apple-silicon
mac-studio
quantized
text-generation
tool-calling
structured-output pipeline_tag: text-generation

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.

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.

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

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

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)