Image-Text-to-Text
MLX
Safetensors
gemma4_unified
quantized
gemma
gemma4
apple-silicon
turboquant
4-bit precision
Instructions to use majentik/gemma-4-12B-TurboQuant-MLX-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/gemma-4-12B-TurboQuant-MLX-MXFP4 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("majentik/gemma-4-12B-TurboQuant-MLX-MXFP4") config = load_config("majentik/gemma-4-12B-TurboQuant-MLX-MXFP4") # 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
- Xet hash:
- d1abc78beb7782116fd141cf428c784c9c7f4d68a8af0ce38641040287a21b49
- Size of remote file:
- 5.37 GB
- SHA256:
- f767a133827d478e5bd72c3c9368d27093f6cae7f9f6ba300e46f24c584cf68b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.