Instructions to use divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-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("divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-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 divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-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 "divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-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 "divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-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 "divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-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 divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "divinetribe/Huihui-gemma-4-31B-it-abliterated-4bit-mlx" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Huihui-gemma-4-31B-it-abliterated — 4-bit MLX for Apple Silicon

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

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

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 Humboldt, CA. Open to work on local-AI and Apple Silicon inference: [email protected].