Instructions to use Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality 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("Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality")
config = load_config("Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality")

# 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 Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality"

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": "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality 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 "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality"

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 Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality

Run Hermes

hermes

OpenClaw new

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality"

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 "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality" \
  --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 MTPLX Optimized Quality

This is the MTPLX Optimized Quality artifact for Qwen3.6 27B.

Policy:

Target trunk: true flat 8-bit MLX affine quantization, group64.
MTP auxiliary tensors (mtp.fc, norms, scales/biases, and non-linear tensors) are preserved in BF16 where applicable.

This is intended as the higher-quality sibling to Qwen3.6-27B-MTPLX-Optimized-Speed. It favors the Flat8 target and calibrated INT8 proposal sidecar instead of the smaller speed-focused artifact.

MTPLX

mtplx start --model Youssofal/Qwen3.6-27B-MTPLX-Optimized-Quality

The artifact includes mtplx_runtime.json and mtp/weights.safetensors, so MTPLX can inspect and route it through the native Qwen MTP backend while generic MLX vision loaders only glob the base model shards.

Local bakeoff snapshot

Measured on the local M5 Max max-fan Flappy 2k depth-3 bakeoff:

Metric	Value
Decode TPS	33.63
Acceptance D1/D2/D3	95.6% / 85.3% / 74.1%
Verify ms/call	88.1 ms
Peak memory	27.62 GiB

This row is a local release-readiness check, not a broad public hardware claim.

Provenance

Base model: Qwen/Qwen3.6-27B
MTPLX staging manifest: mtplx_upload_manifest.json

Vision compatibility

This revision keeps the optimized MTPLX draft sidecar at mtp/weights.safetensors so generic VLM loaders see only the normal Qwen vision/text weight shards at the repository root. The base trunk is the MLX 8-bit Qwen3.6 vision layout; MTPLX reads the draft sidecar through config.json.

Downloads last month: 3,567

Safetensors

Model size

8B params

Tensor type

BF16

U32

MLX

Hardware compatibility

8-bit