Instructions to use mavis-ai/Gemma4-12B-MLX-Q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mavis-ai/Gemma4-12B-MLX-Q8 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/Gemma4-12B-MLX-Q8") config = load_config("mavis-ai/Gemma4-12B-MLX-Q8") # 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
mavis-ai/Gemma4-12B-MLX-Q8
This model is a mixed-policy 8-bit MLX quantized version of the official Google Gemma 4 12B model. No structural modifications or fine-tuning have been applied to the base weights; the package applies MLX quantization while preserving the higher-precision tensor groups indicated by Google's official QAT Q4 GGUF layout.
π‘ Notice for Everyone While this repository is hosted primarily as a dedicated engine source for our application ecosystem (R.E.V.I.S.), you are completely free to download and use this quantized model normally for your own local MLX projects or workflows. For more details on the base model, please refer directly to the Official google/gemma-4-12B page.
π Optimized for R.E.V.I.S. (Local Cognitive OS)
We quantized and thoroughly tested this model to serve as the local reasoning engine for our project: R.E.V.I.S.
R.E.V.I.S. is a 100% local Cognitive OS for Multi-Agentic AI. It transforms your Mac devices into a distributed Agentic Swarm via zero-config Wi-Fi clustering, allowing you to run heavy AI workloads such as recursive web research, dynamic RAG generation, and multi-step logic without killing single-machine performance.
If you are interested in pushing the absolute limits of local AI and open-weight models like this one, check out our project!
- π Official Website: https://mavis-ai.co.jp/revis/
- βΆοΈ Watch the 13-min Raw Demo (Multi-node Dynamic RAG): https://x.gd/LxaBF
- π¦ Follow our updates on X: @mavis_ai_jp
Quantization
This package follows the quantization boundaries used by Google's official Gemma 4 QAT Q4 GGUF release, adapted for a regular Q8 build:
- Default language tensor quantization: MLX affine Q8
- Token embeddings: BF16
- Audio, vision, and multimodal projection components: BF16
- Group size:
64 - Mode:
affine
License
This model is a quantized derivative of Google's Gemma 4 (base model: google/gemma-4-12B), which is released by Google under the Apache License 2.0.
This derivative is likewise distributed under the Apache License 2.0. A copy of the license is included in the LICENSE file in this repository, and can also be found at https://www.apache.org/licenses/LICENSE-2.0.
Modification notice: The base weights have been converted to MLX and quantized with a mixed Q8/BF16 policy. No fine-tuning or structural modifications were made to the model architecture.
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Base model
google/gemma-4-12B