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

Libraries

How to use Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision 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("Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision")
config = load_config("Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision")

# 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 Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision with Pi:

Start the MLX server

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

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": "Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision 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 "Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision"

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 Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision

Run Hermes

hermes

OpenClaw new

How to use Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision with OpenClaw:

Start the MLX server

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

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 "Jonandrop/Qwen3.6-27B-MTPLX-Optimized-Speed-Vision" \
  --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 Speed (Vision-Enabled)

A unified local model that both codes at the Qwen3.6-27B level and processes images/videos, packaged for Apple Silicon via MTPLX native speculative decoding with Multi-Token-Prediction (MTP).

This repository restores the multimodal vision tower that is typically stripped in text-only MLX/MTPLX conversions.

Architecture

Body: 4-bit affine, group size 64 (from Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed)
MTP sidecar (mtp/weights.safetensors): Prequantized MTP heads with a 3-bit affine group-64 draft-only LM head.
Vision tower (vision_tower.safetensors, 333 tensors, bf16 unquantized): Extracted verbatim from the official checkpoint's own model.visual.* weights (shards 7 and 8 of Qwen/Qwen3.6-27B) and renamed to the vision_tower.* prefix for MTPLX native VLM compatibility (~879 MB).

Usage (MTPLX)

mtplx start --model <path-to-this-model-dir> --port 8092 \
  --chat-template-path <path-to-this-model-dir>/chat_template.jinja

Serves an OpenAI-compatible endpoint supporting both text and image input payloads via POST /v1/chat/completions.

The MTPLX CLI defaults to mtp_history_policy=committed, which this model requires for healthy deep-position acceptance. The model's recommended draft sampler is temperature 0.7 (see recommended_draft_sampler in mtplx_runtime.json); pass --draft-temperature 0.7 to match.

Performance (measured)

Apple M5 Pro 64 GB, MTPLX 1.0.4, temperature 0.6, mtp_history_policy=committed, --draft-temperature 0.7 (model recommended), thinking OFF, warm (4 warmup prompts discarded), 8 measured prompts from the calibration_coding suite, max_tokens=192, median tok/s.

Depth	tok/s (wall-clock e2e)	tok/s (decode-only)	speedup vs AR (e2e)	acceptance pos1/2/3
AR (`--no-mtp`)	13.7	14.5	1.00x	-
MTP depth 1	19.7	20.9	1.44x	0.923
MTP depth 2	19.9	20.8	1.46x	0.950 / 0.753
MTP depth 3	18.1	19.7	1.32x	0.900 / 0.759 / 0.648

Two throughput definitions are reported:

tok/s (wall-clock e2e) = generated_tokens / total_elapsed, includes prompt prefill. The real end-to-end speed and the honest denominator for the speedup ratio.
tok/s (decode-only) = generated_tokens / decode_elapsed, excludes prefill.

Acceptance is stable across runs: greedy draft (temp 0.0) gives 0.874/0.740/0.636, recommended draft (temp 0.7) gives 0.900/0.759/0.648, both within noise. Vision graft has zero acceptance cost relative to the text-only source (Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed measures identical acceptance 0.874/0.740/0.636 under the same config).

Earlier baked mtplx_runtime.json figures (acceptance 1.0/0.98/0.94, ~63 tok/s) were measured on Apple M5 Max 128 GB and are not reproducible on M5 Pro 64 GB due to lower memory bandwidth; they are preserved in the historical_m5max field of mtplx_runtime.json for traceability. The vision tower is lazy-loaded (only when a request carries an image), so text-only throughput is unaffected by it. Numbers are indicative, not a benchmark suite.

Sources And Attribution

Component	Source	Revision / License
Base model	`Qwen/Qwen3.6-27B`	Apache-2.0
Quantized Text/MTP repo	`Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed`	Apache-2.0
MTPLX conversion	`youssofal/mtplx`	Apache-2.0

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Safetensors

Model size

27B params

Tensor type

BF16

U32

MLX

Hardware compatibility

Quantized

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