Uncensored Gemma 4 31B, local on Apple Silicon. A 4-bit MLX conversion of huihui-ai/Huihui-gemma-4-31B-it-abliterated, the abliterated Gemma 4 31B Instruct. 17.3 GB on disk — wants a 32 GB Mac. No cloud, no API key, no refusals.


Size on disk	17.3 GB (4 shards)
Mac RAM	32 GB recommended, 24 GB for short contexts
Architecture	Gemma 4

What "abliterated" means

The refusal direction has been orthogonalized out of the weights, so the model answers instructions a stock instruct-tuned model would decline, while staying coherent on ordinary tasks. The abliteration here is huihui-ai's, not mine — this repo is the MLX conversion of their work.

This is an uncensored model. You are responsible for how you use it and for complying with the base model's license and applicable law.

Quick start

pip install mlx-lm

bash

mlx_lm.generate --model divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx \
  --prompt "Explain quantum entanglement to a 12 year old." --max-tokens 400

python

from mlx_lm import load, generate
model, tok = load("divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx")
text = tok.apply_chat_template(
    [{"role": "user", "content": "Explain quantum entanglement to a 12 year old."}],
    add_generation_prompt=True, tokenize=False)
print(generate(model, tok, prompt=text, max_tokens=400))

Also loads in LM Studio and anything else that reads MLX models.

Conversion details

Quantization: 4-bit affine, group size 64, via mlx_lm.convert
Format: MLX safetensors — no GGUF, no llama.cpp, no GPU needed
Runs on: M1 / M2 / M3 / M4 / M5 Macs, entirely on-device

More abliterated MLX models

Part of the Abliterated MLX for Apple Silicon collection — Llama 3.3 70B, Gemma 4, Qwen3, Qwen3-VL, Hermes 4 and Muse Glimmer, all converted for Apple Silicon.

Credit

Base model and abliteration by huihui-ai. MLX 4-bit conversion by divinetribe.

Part of Claude Code Local

This model is one of the fighters in Claude Code Local (3.2k★), which runs Claude Code 100% on-device on Apple Silicon through an MLX-native Anthropic-API server. Not sure which local model to run as an agent? Check the Agent-12 local agent leaderboard: real agent tasks, judged by the filesystem, same hardware for every row.

Built by Matt Macosko in Arcata, CA. Open to work on local-AI and Apple Silicon inference: matt@ineedhemp.com.