Instructions to use divinetribe/Qwen3.6-27B-abliterated-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use divinetribe/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-abliterated-4bit-mlx"

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/Qwen3.6-27B-abliterated-4bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use divinetribe/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-abliterated-4bit-mlx

Run Hermes

hermes

MLX LM

How to use divinetribe/Qwen3.6-27B-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/Qwen3.6-27B-abliterated-4bit-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "divinetribe/Qwen3.6-27B-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/Qwen3.6-27B-abliterated-4bit-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.6-27B-abliterated-4bit-mlx

A 4-bit MLX build of huihui-ai's abliterated Qwen3.6 27B, packaged for Apple Silicon. At ~15 GB it runs comfortably on a 32 GB Mac — the new sweet spot in the divinetribe roster.

Credit where it's due

The hard part — the abliteration (removing the refusal direction) — was done by huihui-ai. This repo is only the MLX 4-bit conversion of their work, so Apple Silicon users get a one-command local pull. All the uncensoring credit goes to huihui-ai; please go like and follow their work:

Base model: huihui-ai/Huihui-Qwen3.6-27B-abliterated

This build

Quantization: 4-bit, group size 64 (MLX)
Architecture: Qwen3.6 (qwen3_5) — converted with mlx-lm
Footprint: ~15 GB — fits a 32 GB Mac with room to work

Use with MLX

pip install mlx-lm
python -m mlx_lm.generate --model divinetribe/Qwen3.6-27B-abliterated-4bit-mlx --prompt "Hello" --max-tokens 256

Drop-in for the claude-code-local stack — point MLX_MODEL at this repo.

Note

"Abliterated" suppresses the model's built-in refusal direction so it won't refuse benign-but-edgy requests. It is not a capability upgrade, and you remain bound by the upstream Qwen license. Use it responsibly.

Abliteration by huihui-ai. MLX conversion + quantization by divinetribe. Part of the Abliterated MLX for Apple Silicon collection.