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

Libraries

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed 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("Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed")

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
LM Studio

Pi new

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

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-Speed"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

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-Speed

Run Hermes

hermes

MLX LM

How to use Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.6-27B MTPLX Optimized Speed

Run this with MTPLX

MTPLX is an MLX-native runtime for native Multi-Token-Prediction speculative decoding on Apple Silicon. Up to 2.24× faster decode at real coding temperatures (temp=0.6 / top_p=0.95 / top_k=20) using the model's own built-in MTP heads — no external drafter, no greedy hack.

pip install mtplx
mtplx start

Project: github.com/youssofal/MTPLX

Other MTPLX checkpoints:

Qwen3.6-27B-MTPLX-Optimized — verified default (GDN8-Speed4 trunk + CyanKiwi INT4 MTP)
Qwen3.5-4B-MTPLX-Optimized-Speed — small 4-bit speed-test
Qwen3.5-4B-Optimized-MTPLX — small 8-bit

This is the speed checkpoint for MTPLX v0.1.0-preview. It combines the MLXCommunity flat 4-bit MLX trunk with the calibrated CyanKiwi INT4 MTP sidecar and embeds mtplx_runtime.json so MTPLX can run it as the optimized speed variant.

Target repository: Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed

Runtime

mtplx pull Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed
mtplx run "hello" --model Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed
mtplx serve --model Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed --port 8000 --no-stats-footer

MTPLX v0.1.0-preview supports Qwen3-Next-MTP only. This model is verified for the qwen3-next-mtp backend.

Sources And Attribution

Component	Source	Revision	License
Base model	`Qwen/Qwen3.6-27B`	`6a9e13bd6fc8f0983b9b99948120bc37f49c13e9`	Apache-2.0
Calibrated MTP sidecar source	`cyankiwi/Qwen3.6-27B-AWQ-BF16-INT4`	`8cc526ddcfdd53346f94fb9b5458ca8495a63218`	Apache-2.0
MTPLX conversion and runtime contract	`youssofal/mtplx`	v0.1.0-preview.1	Apache-2.0

The trunk is the MLXCommunity flat 4-bit affine conversion of Qwen/Qwen3.6-27B. The MTP sidecar is derived from the downloaded CyanKiwi compressed-tensors checkpoint at the revision listed above.

Verification

mtplx_runtime.json records:

architecture: qwen3-next-mtp
maximum MTP depth: 3
recommended profile: performance-cold
recommended draft-only LM head: 3-bit affine, group_size=64
recommended draft sampler: temperature 0.70, top-p 0.95, top-k 20
exactness gate: Phase 0H paged-verifier smoke
exactness max absolute diff: 0.0
verified hardware: Apple M5 Max, 128 GB unified memory
verified timestamp: 2026-05-03T23:07:00+0100

Target sampler used for the recorded contract: temperature 0.6, top-p 0.95, top-k 20, with thinking mode disabled. The draft proposal sampler is intentionally slightly hotter at temperature 0.70; target verification and residual correction remain exact.

Performance Honesty

This is the default speed lane. On the local Apple M5 Max fanmax performance-cold benchmark, this artifact reached 63.056 tok/s and 62.886 tok/s at depth 3 on the long-code 192-token prompt when using its contract-recommended 3-bit draft-only LM head plus draft sampler temperature 0.70, with thinking mode disabled and foreground app load reduced. The proposal change reduced the measured correction tokens from 6 to 3 and verify calls from 50 to 49 on the recorded prompt; target sampling remains temperature 0.6 / top-p 0.95 / top-k 20. A full greedy diagnostic on the same cleaned fanmax window measured 60.108 tok/s. Earlier contract evidence measured 61.527 and 60.900 tok/s, the earlier 3-bit draft-head contract measured 60.038 and 60.061 tok/s, and the older 4-bit draft-only LM head measured 57.668 tok/s on the same lane.

Files

model-*.safetensors: MLXCommunity flat 4-bit trunk shards.
mtp.safetensors: calibrated prequantized INT4 MTP sidecar.
mtplx_runtime.json: MTPLX verified runtime contract.
MTPLX_PUBLISH_MANIFEST.json: local staging manifest with source paths, materialization method, sizes, and optional hashes.

Model tree for Youssofal/Qwen3.6-27B-MTPLX-Optimized-Speed

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Qwen3.6-27B-MTPLX-Optimized-Speed