Instructions to use deepsweet/Qwen3.6-27B-MLX-VL-oQ6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use deepsweet/Qwen3.6-27B-MLX-VL-oQ6 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("deepsweet/Qwen3.6-27B-MLX-VL-oQ6") 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 deepsweet/Qwen3.6-27B-MLX-VL-oQ6 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "deepsweet/Qwen3.6-27B-MLX-VL-oQ6"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "deepsweet/Qwen3.6-27B-MLX-VL-oQ6" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use deepsweet/Qwen3.6-27B-MLX-VL-oQ6 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "deepsweet/Qwen3.6-27B-MLX-VL-oQ6"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "deepsweet/Qwen3.6-27B-MLX-VL-oQ6" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepsweet/Qwen3.6-27B-MLX-VL-oQ6", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use deepsweet/Qwen3.6-27B-MLX-VL-oQ6 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 "deepsweet/Qwen3.6-27B-MLX-VL-oQ6"
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 deepsweet/Qwen3.6-27B-MLX-VL-oQ6
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use deepsweet/Qwen3.6-27B-MLX-VL-oQ6 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "deepsweet/Qwen3.6-27B-MLX-VL-oQ6"
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 "deepsweet/Qwen3.6-27B-MLX-VL-oQ6" \ --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"
Version that preserves MTP Heads
As of v0.3.9 oMLX supports native MTP generation. This is very helpful for the 27B dense model token generation. Any chance we can get a version that preserves the MTP headers.
Jundot has a version but the model card is very bare on details. (https://huggingface.co/Jundot/Qwen3.6-27B-oQ6-mtp). Do you think this is sufficient? I like that you have text and vision versions.
Additionally, I love your KL Divergence graphs and explanation of "fp16", thanks for that!!
Hi.
Jundot is the author of oMLX and oQ, his uploads are definitely trustworthy.
As for MTP – I can upload a text-only Qwen3.6-27B-MLX-oQ6-MTP if you need it.
Thanks! I don't want to bother you, I am fine using Jundot's! Appreciate your willingness.