Instructions to use QuantLLM/functiongemma-270m-it-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use QuantLLM/functiongemma-270m-it-4bit-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir functiongemma-270m-it-4bit-mlx QuantLLM/functiongemma-270m-it-4bit-mlx
- Transformers
How to use QuantLLM/functiongemma-270m-it-4bit-mlx with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantLLM/functiongemma-270m-it-4bit-mlx", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Xet hash:
- 46a1b188bd8d27933dc7719e8d74be2ec741d46e44746009e40306480ce2d7b4
- Size of remote file:
- 436 MB
- SHA256:
- 087d6ea1c2deedfe75b06ae101c94b27a55a05c48998535ea62be592e315a698
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