Instructions to use OmTheLast/jee-solver-experimental-mlx-adapters with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OmTheLast/jee-solver-experimental-mlx-adapters 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("OmTheLast/jee-solver-experimental-mlx-adapters") config = load_config("OmTheLast/jee-solver-experimental-mlx-adapters") # 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
Download __pycache__/load_mlx.cpython-313.pyc from OmTheLast/jee-solver-experimental-mlx-adapters: direct link, hf CLI and curl.
- Browser
- Download file 6.7 kB
-
https://huggingface.co/OmTheLast/jee-solver-experimental-mlx-adapters/resolve/main/__pycache__/load_mlx.cpython-313.pyc
- Command line
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hf download hf://OmTheLast/jee-solver-experimental-mlx-adapters/__pycache__/load_mlx.cpython-313.pyc
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curl -L -o load_mlx.cpython-313.pyc https://huggingface.co/OmTheLast/jee-solver-experimental-mlx-adapters/resolve/main/__pycache__/load_mlx.cpython-313.pyc
6.7 kB
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