Image-Text-to-Text
MLX
Core ML
Safetensors
mobile-o
multimodal
unified-model
ios
on-device
mobile
edge-ai
Instructions to use Amshaker/Mobile-O-0.5B-iOS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Amshaker/Mobile-O-0.5B-iOS 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("Amshaker/Mobile-O-0.5B-iOS") config = load_config("Amshaker/Mobile-O-0.5B-iOS") # 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
- Atomic Chat
- Xet hash:
- a84a5edbee0169bcd4d51c4ce72b9994efcb6e312aa18f945402054b107999ef
- Size of remote file:
- 361 kB
- SHA256:
- e63c9445eca87db9d06c53e7c54c2622b0f623549fd2a519dafdf0c72c60f9e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.