Instructions to use harishkumar12k/gemma-4-E2B-it-mlx-2Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harishkumar12k/gemma-4-E2B-it-mlx-2Bit with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("harishkumar12k/gemma-4-E2B-it-mlx-2Bit") model = AutoModelForImageTextToText.from_pretrained("harishkumar12k/gemma-4-E2B-it-mlx-2Bit") - MLX
How to use harishkumar12k/gemma-4-E2B-it-mlx-2Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gemma-4-E2B-it-mlx-2Bit harishkumar12k/gemma-4-E2B-it-mlx-2Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- c62336ad134cad6f154d84eb0e5a5fa9ca17cd665ef3ba5ac4fd02b1486760b4
- Size of remote file:
- 32.2 MB
- SHA256:
- cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
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