Instructions to use trevon/Qwen3.6-27B-mtp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use trevon/Qwen3.6-27B-mtp 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("trevon/Qwen3.6-27B-mtp") 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 Settings
- LM Studio
- Pi
How to use trevon/Qwen3.6-27B-mtp with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "trevon/Qwen3.6-27B-mtp"
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": "trevon/Qwen3.6-27B-mtp" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use trevon/Qwen3.6-27B-mtp 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 "trevon/Qwen3.6-27B-mtp"
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 trevon/Qwen3.6-27B-mtp
Run Hermes
hermes
- MLX LM
How to use trevon/Qwen3.6-27B-mtp with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "trevon/Qwen3.6-27B-mtp"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "trevon/Qwen3.6-27B-mtp" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trevon/Qwen3.6-27B-mtp", "messages": [ {"role": "user", "content": "Hello"} ] }'
Qwen3.6-27B-mtp (affine-8)
MLX conversion of Qwen/Qwen3.6-27B, affine 8-bit (group_size 64), with the native Multi-Token-Prediction (MTP) head embedded in the main weight shards for native speculative decoding.
What changed from the previous packaging
This release replaces the previous mxfp8 packaging, which produced a ~29% slowdown when --mtp was enabled. Two compounding causes:
- mxfp8 backbone: the speculative verify pass processes 2 tokens through the full backbone every step. mxfp8's per-call dequant overhead does not amortize across that path on Apple Silicon — re-quantizing the same artifact to affine-8 reduced the regression from 29% to 10%.
- Lossy round-trip in the original packaging: even after re-quantization, the MTP head's calibration was damaged enough that MTP remained a net regression. A fresh affine-8 conversion straight from
Qwen/Qwen3.6-27B(this artifact) restores +48% with--mtp.
This release is therefore a fresh affine-8 conversion from the upstream Qwen base, not a re-packaging of the previous mxfp8 artifact.
Conversion command
mlx_lm.convert --hf-path Qwen/Qwen3.6-27B \
--mlx-path Qwen3.6-27B-mtp \
-q --q-mode affine --q-bits 8 --q-group-size 64
Run
Without MTP (stock mlx-lm from PyPI):
mlx_lm.generate --model trevon/Qwen3.6-27B-mtp \
--prompt "..." --max-tokens 100
With MTP (AirRunner feat/mtp-native, PR 990):
git clone https://github.com/AirRunner/mlx-lm.git
cd mlx-lm && git checkout feat/mtp-native
uv venv && uv pip install -e .
mlx_lm.generate --model trevon/Qwen3.6-27B-mtp \
--prompt "..." --max-tokens 100 --mtp
Benchmarks (Apple M4 Max)
| Mode | tokens/sec |
|---|---|
no --mtp |
15.1 |
--mtp |
22.4 (+48%) |
License: per upstream Qwen/Qwen3.6-27B.
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