Instructions to use divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text 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/gemma-4-12B-it-abliterated-4bit-mlx-text")

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/gemma-4-12B-it-abliterated-4bit-mlx-text 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/gemma-4-12B-it-abliterated-4bit-mlx-text"

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": "divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text 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/gemma-4-12B-it-abliterated-4bit-mlx-text"

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/gemma-4-12B-it-abliterated-4bit-mlx-text" \
  --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"

MLX LM

How to use divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text 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/gemma-4-12B-it-abliterated-4bit-mlx-text"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text 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/gemma-4-12B-it-abliterated-4bit-mlx-text"

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/gemma-4-12B-it-abliterated-4bit-mlx-text

Run Hermes

hermes

Atomic Chat

Gemma 4 12B IT — Abliterated — 4-bit MLX (text-only)

Uncensored Gemma 4 12B that loads on the current pip install mlx-lm release. Other 12B MLX builds still stop with Model type gemma4_unified not supported on mlx-lm 0.31.3, the newest version on PyPI. This one is stripped to the text tower and relabeled gemma4, so it loads with no patching. 11 GB, 24 GB Mac.

A text-only 4-bit MLX build of Gemma 4 12B (abliterated) that loads directly under mlx_lm — the same way divinetribe/gemma-4-31b-it-abliterated-4bit-mlx does.

Why this exists

Google ships google/gemma-4-12B-it with model_type: gemma4_unified (text + vision + audio in one checkpoint), while the 31B ships as plain model_type: gemma4. mlx_lm has a gemma4 loader but no gemma4_unified loader in the current PyPI release (0.31.3, uploaded 2026-04-22), so every standard 12B MLX build (including the 4-bit/8-bit/bf16 mlx-community ones) fails there with:

ValueError: Model type gemma4_unified not supported.

Upstream status (checked 2026-08-12): this was fixed on mlx-lm main in #1349 (merged 2026-06-05), which remaps gemma4_unified to the gemma4 loader and strips the towers in sanitize(). It has not shipped to PyPI yet, so pip install mlx-lm still hits the error above. Once a release carries it, the standard mlx-community builds will load too and this repo's advantage becomes just the abliteration.

Switching the loader to mlx_vlm then hangs, because it tries to spin up vision and audio towers a text-only server never feeds.

This repo fixes that by stripping the multimodal towers (vision_embedder, embed_vision, embed_audio, …) and relabeling model_type: gemma4, leaving only the language_model.* tensors. The 4-bit abliterated weights are preserved byte-for-byte — nothing is re-quantized. The result loads in plain mlx_lm like any text model.

Use

pip install mlx-lm

from mlx_lm import load, generate
model, tok = load("divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text")
print(generate(model, tok, prompt="Hello", max_tokens=128))

Or point an MLX server at it:

MLX_MODEL=divinetribe/gemma-4-12B-it-abliterated-4bit-mlx-text

Runs comfortably on a 32 GB Apple-Silicon Mac (≈7 GB weights, 4-bit).

Provenance

Derived from divinetribe/gemma-4-12B-it-abliterated-4bit-mlx — same abliteration, text tower only. Base: google/gemma-4-12B-it.

More abliterated MLX models

Part of the Abliterated MLX for Apple Silicon collection — 11 uncensored models converted for Macs, from Gemma 4 12B up to Llama 3.3 70B, plus Qwen3, Qwen3-VL, Hermes 4 and Muse Glimmer 30B.