Instructions to use mavis-ai/Qwen3.6-35B-MoE-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mavis-ai/Qwen3.6-35B-MoE-Q4 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("mavis-ai/Qwen3.6-35B-MoE-Q4") config = load_config("mavis-ai/Qwen3.6-35B-MoE-Q4") # 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 mavis-ai/Qwen3.6-35B-MoE-Q4 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mavis-ai/Qwen3.6-35B-MoE-Q4"
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": "mavis-ai/Qwen3.6-35B-MoE-Q4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mavis-ai/Qwen3.6-35B-MoE-Q4 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 "mavis-ai/Qwen3.6-35B-MoE-Q4"
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 mavis-ai/Qwen3.6-35B-MoE-Q4
Run Hermes
hermes
- OpenClaw new
How to use mavis-ai/Qwen3.6-35B-MoE-Q4 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "mavis-ai/Qwen3.6-35B-MoE-Q4"
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 "mavis-ai/Qwen3.6-35B-MoE-Q4" \ --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"
mavis-ai/Qwen3.6-35B-MoE-Q4
This repository contains an MLX-ready Q4 quantized build of Qwen3.6-35B-A3B, prepared for local inference on Apple Silicon Macs.
This is not a newly fine-tuned model. It is a local MLX redistribution converted from the official Qwen3.6-35B-A3B checkpoint. No additional training or architecture-level modification has been applied.
Source revision: 995ad96eacd98c81ed38be0c5b274b04031597b0.
Important Notice
This package uses a controlled, Gemma4-like mixed quantization policy. It is not a plain "quantize every linear layer to Q4" build.
For the original model card, architecture details, intended usage, limitations, and evaluation information, refer to the official upstream model:
- Base model: https://huggingface.co/Qwen/Qwen3.6-35B-A3B
Quantization
This package uses MLX affine quantization with a mixed layout intended to preserve quality-sensitive tensor groups:
- Source checkpoint:
Qwen/Qwen3.6-35B-A3B - Default language tensor quantization: MLX affine Q4
- Token embeddings: MLX affine Q6
- LM head: MLX affine Q6
- MoE routing gates and shared expert gates: BF16
- Vision / multimodal components: BF16
- Group size:
64 - Mode:
affine
The goal is to keep the model closer to the higher-precision routing and multimodal behavior expected from the original checkpoint while still reducing local storage and memory requirements. For this Q4 build, vocabulary-facing tensors are kept at MLX affine Q6.
Model Notes
Qwen3.6-35B-A3B is a multimodal Mixture-of-Experts model with 35B total parameters and approximately 3B activated parameters. The local source checkpoint reports:
- Hidden size:
2048 - Layers:
40 - Experts:
256 - Activated experts:
8routed experts plus shared expert - Context length:
262144
Usage
Install or update the MLX runtime used for Qwen3.6 / multimodal models:
pip install -U mlx mlx-lm mlx-vlm huggingface_hub hf_xet
Download the model:
hf download mavis-ai/Qwen3.6-35B-MoE-Q4 \
--local-dir ~/Models/mlx/Qwen3.6-35B-MoE-Q4
Run a quick text generation test:
python -m mlx_vlm.generate \
--model ~/Models/mlx/Qwen3.6-35B-MoE-Q4 \
--max-tokens 128 \
--temperature 0.0 \
--prompt "Answer briefly: what can you do?"
Files
Recommended repository files:
.gitattributes
LICENSE
README.md
chat_template.jinja
config.json
configuration.json
generation_config.json
merges.txt
model-00001-of-00004.safetensors
model-00002-of-00004.safetensors
model-00003-of-00004.safetensors
model-00004-of-00004.safetensors
model.safetensors.index.json
preprocessor_config.json
processor_config.json
tokenizer.json
tokenizer_config.json
video_preprocessor_config.json
vocab.json
License
This repository redistributes a quantized derivative of Qwen3.6-35B-A3B, which is distributed under the Apache License 2.0. A copy of the license is included in the LICENSE file in this repository.
Modification Notice
Compared with the official Qwen source checkpoint, this repository applies the following packaging modification:
The source checkpoint was converted to MLX format and quantized with a controlled mixed Q4/Q6/BF16 policy for local MLX inference.
No fine-tuning, additional training, or architecture-level modification has been applied.
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