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 ofsupergemma4-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.