Instructions to use deanzchen/Qwen3.6-27B-Q5-MTPLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use deanzchen/Qwen3.6-27B-Q5-MTPLX 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("deanzchen/Qwen3.6-27B-Q5-MTPLX") config = load_config("deanzchen/Qwen3.6-27B-Q5-MTPLX") # 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
- Pi
How to use deanzchen/Qwen3.6-27B-Q5-MTPLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "deanzchen/Qwen3.6-27B-Q5-MTPLX"
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": "deanzchen/Qwen3.6-27B-Q5-MTPLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use deanzchen/Qwen3.6-27B-Q5-MTPLX 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 "deanzchen/Qwen3.6-27B-Q5-MTPLX"
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 deanzchen/Qwen3.6-27B-Q5-MTPLX
Run Hermes
hermes
- OpenClaw new
How to use deanzchen/Qwen3.6-27B-Q5-MTPLX with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "deanzchen/Qwen3.6-27B-Q5-MTPLX"
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 "deanzchen/Qwen3.6-27B-Q5-MTPLX" \ --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 Q5 + MTPLX MTP
This artifact combines a uniform 5-bit MLX Qwen3.6 27B trunk with the calibrated MTPLX MTP sidecar used by the optimized-speed release.
Components
- Base model:
Qwen/Qwen3.6-27B - Q5 trunk:
mlx-community/Qwen3.6-27B-5bit, revision46f9b268ffe53528a8ad4ce8f684a480b3ef0d18 - MTP sidecar:
Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed, revisionbe5190f2349594ec941753efc90a4ca5641af174 - Trunk quantization: MLX affine 5-bit, group size 64
- MTP sidecar path:
mtp/weights.safetensors - MTPLX discovery metadata:
mtplx_runtime.json
The Q5 trunk and optimized MTP sidecar are family-compatible but do not have a
published exactness contract for this combined artifact. MTPLX therefore marks
it family-compatible-unverified and requires explicit unsafe confirmation.
Run with MTPLX
export MTPLX_VLLM_METAL_PAGED_TURBOQUANT=1
export MTPLX_VLLM_METAL_PAGED_TURBOQUANT_K_QUANT=q8_0
export MTPLX_VLLM_METAL_PAGED_TURBOQUANT_V_QUANT=q4_0
export MTPLX_PREFILL_CHUNK_SIZE=512
export MTPLX_PREFILL_CHUNK_SIZE_DENSE=512
export MTPLX_PREFILL_CHUNK_SIZE_REPAGE=512
mtplx serve \
--model deanzchen/Qwen3.6-27B-Q5-MTPLX \
--profile sustained \
--generation-mode mtp \
--depth 2 \
--context-window 262144 \
--prefill-chunk-tokens 512 \
--temperature 0.6 \
--top-p 0.95 \
--top-k 20 \
--draft-temperature 0.7 \
--draft-top-p 0.95 \
--draft-top-k 20 \
--reasoning auto \
--preserve-thinking scoped \
--ssd-session-cache off \
--no-stats-footer \
--unsafe-force-unverified \
--yes
TurboQuant normally activates after the sustained cache crosses 131,072 tokens. The configured 262,144-token maximum is a capacity target; this Q5 combination has been exercised to 132,515 tokens, not to the full maximum.
Local M5 Max measurements
Hardware: M5 Max, 40 GPU cores, 48 GB unified memory. Runtime: MTPLX 2.0.2, MLX 0.31.2, MLX-LM 0.31.3.
| Mode | Decode speed |
|---|---|
| Autoregressive | 25.15 tok/s |
| MTP depth 1 | 41.29 tok/s |
| MTP depth 2 | 49.91 tok/s |
| MTP depth 3 | 44.95 tok/s |
Depth 2 was selected. The matched short run used approximately 22.1 GB peak memory. A 132,515-token activation probe reached approximately 35.7 GB and confirmed TurboQuant K8/V4, but initial decode at that extreme context was only about 0.08 tok/s.
These are local measurements, not broad hardware claims.
The current CLI wrapper defers full verification-hidden materialization and evaluates the committed hidden-state slice after target distributions. Five token-identical D2 runs averaged 50.78 tok/s; the persisted wrapper's final gate measured 49.82 tok/s with 100% acceptance.
For repeatable expectations and regression thresholds, see
MTPLX_PERFORMANCE_BASELINE.json. An 8-token smoke test is informational; use
the 128- or 256-token direct tuner workload as the performance gate.
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