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:
- 1a3936df7d8ee4b3f63cea9e8b2eed9405c3503cc3ad3d0525505b5885a0f92a
- Size of remote file:
- 1,000 MB
- SHA256:
- 5e6aff45aef8a31e39e22b22cbf78f3b747750094c45311e582e20443b6e0a3f
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