Instructions to use mavis-ai/Gemma4-31B-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mavis-ai/Gemma4-31B-MLX 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/Gemma4-31B-MLX") config = load_config("mavis-ai/Gemma4-31B-MLX") # 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
- LM Studio
- Pi
How to use mavis-ai/Gemma4-31B-MLX 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/Gemma4-31B-MLX"
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/Gemma4-31B-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mavis-ai/Gemma4-31B-MLX 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/Gemma4-31B-MLX"
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/Gemma4-31B-MLX
Run Hermes
hermes
mavis-ai/Gemma4-31B-MLX
This repository contains the exact, unmodified base weights of the official Google Gemma 4 31B model.
⚠️ Important Notice While this repository is hosted primarily as a dedicated download source for our application ecosystem (R.E.V.I.S.), you are completely free to download and use these weights normally for your own MLX or local AI workflows. The weights are 100% identical to the official release. For all details regarding the model architecture, capabilities, and official documentation, you must refer directly to the Official google/gemma-4-31B page.
🚀 Optimized for R.E.V.I.S. (Local Cognitive OS)
We host 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—like 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!
- 🌐 Official Website: https://mavis-ai.co.jp/revis/
- ▶️ Watch the 13-min Raw Demo (Multi-node Dynamic RAG): https://x.gd/LxaBF
- 🐦 Follow our updates on X: @mavis_ai_jp
License
This repository redistributes Google's Gemma 4 31B model, which is released by Google under the Apache License 2.0 (base model: google/gemma-4-31B).
These weights are redistributed unmodified under the same 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.
Notice: The weights in this repository are identical to the official release; no fine-tuning, quantization, or structural modifications have been applied.
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