Image-Text-to-Text
Safetensors
MLX
mlx-vlm
gemma4_unified
gemma-4
vision-language
quantized
4-bit precision
6-bit
8-bit precision
apple-silicon
Instructions to use chanderbalaji/Grug-12B-VLM-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use chanderbalaji/Grug-12B-VLM-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("chanderbalaji/Grug-12B-VLM-MLX") config = load_config("chanderbalaji/Grug-12B-VLM-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 Settings
- LM Studio

- Xet hash:
- 6555c2cc854aaf5f28b9cdbb228a25f66c92c7c02cb7f54cf4a1de4780b23a7d
- Size of remote file:
- 169 kB
- SHA256:
- 6fd9edc756a819c3328b2b2b95f70066e00f11512c084ab31924f4e96dd4253f
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