Instructions to use mlx-community/Qwen3.6-27B-MTP-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Qwen3.6-27B-MTP-4bit 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("mlx-community/Qwen3.6-27B-MTP-4bit") 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
How to use mlx-community/Qwen3.6-27B-MTP-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mlx-community/Qwen3.6-27B-MTP-4bit"
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": "mlx-community/Qwen3.6-27B-MTP-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/Qwen3.6-27B-MTP-4bit 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 "mlx-community/Qwen3.6-27B-MTP-4bit"
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 mlx-community/Qwen3.6-27B-MTP-4bit
Run Hermes
hermes
- MLX LM
How to use mlx-community/Qwen3.6-27B-MTP-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/Qwen3.6-27B-MTP-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Qwen3.6-27B-MTP-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Qwen3.6-27B-MTP-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
Qwen3.6-27B-MTP-4bit
This repository contains Multi-Token Prediction (MTP) drafter weights split from Qwen/Qwen3.6-27B for use with mlx-vlm speculative decoding.
This is not a standalone chat or text-generation model. Load it as the draft model alongside a compatible Qwen3.6 27B target checkpoint.
Use with mlx-vlm
uv run mlx_vlm.generate \
--model mlx-community/Qwen3.6-27B-4bit \
--draft-model mlx-community/Qwen3.6-27B-MTP-4bit \
--prompt "Hi, how are you?" \
--max-tokens 256 \
--enable-thinking
For local weights:
uv run mlx_vlm.generate \
--model /path/to/target-model \
--draft-model /path/to/Qwen3.6-27B-mtp-4bit \
--prompt "Hi, how are you?" \
--max-tokens 256 \
--enable-thinking
Model Details
- Model type:
qwen3_5_mtp - MTP block size:
2 - Target architecture: Qwen3.6 27B
- Precision: MLX affine 4-bit, group size 64
- Runtime: MLX /
mlx-vlm - Format: Safetensors with MLX-compatible config and tokenizer files
The stored tensors use MLX affine 4-bit quantization as described in config.json.
Intended Use
Use this repo only as a speculative decoding drafter for compatible Qwen3.6 27B checkpoints. The target model verifies drafted tokens, while this MTP model proposes candidate tokens per decoding step.
Limitations
This checkpoint requires runtime support for Qwen/DeepSeek MTP draft models in mlx-vlm. Standard standalone generation through generic Transformers APIs is not expected to work with this repository by itself.
Please refer to the upstream Qwen/Qwen3.6-27B model card and license terms for model usage constraints.
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Qwen/Qwen3.6-27B