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

Libraries

How to use majentik/Qwen3.6-27B-MLX-4bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("majentik/Qwen3.6-27B-MLX-4bit")
config = load_config("majentik/Qwen3.6-27B-MLX-4bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use majentik/Qwen3.6-27B-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Qwen3.6-27B-MLX-4bit"

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": "majentik/Qwen3.6-27B-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Qwen3.6-27B-MLX-4bit 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 "majentik/Qwen3.6-27B-MLX-4bit"

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 majentik/Qwen3.6-27B-MLX-4bit

Run Hermes

hermes

OpenClaw new

How to use majentik/Qwen3.6-27B-MLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Qwen3.6-27B-MLX-4bit"

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 "majentik/Qwen3.6-27B-MLX-4bit" \
  --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"

Qwen3.6-27B-MLX-4bit

Summary

MLX 4bit (affine, 4-bit, group size 64) quantization of Qwen/Qwen3.6-27B at upstream revision 6a9e13bd6fc8f0983b9b99948120bc37f49c13e9, produced directly from the BF16 safetensors with pipelines.mlx_direct_quantize (majek repo). ~15.2 GiB on disk.

Runtime caveat: this is a weights + config pack published ahead of runtime availability; runtime support pending upstream mlx-lm support for the qwen3_5 vision-language architecture. No local inference smoke was performed on these artifacts, so the quality of this variant is not verified.

Why this variant

Affine 4-bit, group size 64 — the standard MLX deployment tier and the usual quality/size sweet spot.

Reproduce

python -m pipelines.mlx_direct_quantize --model qwen3.6-27b --base-dir /tmp/mlx-direct-release/qwen3.6-27b/base --out-dir /tmp/mlx-direct-release/qwen3.6-27b/4bit --bits 4 --mode affine --group-size 64

Vision tower

The model.visual.* tower (333 tensors) is passed through unquantized in BF16 — only the text tower (model.language_model.*, lm_head.*) is quantized.