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:
- c3f6b8e2c55a57217a6f52ca3d1b18ba55b892111c045934db95dd03120dbbc9
- Size of remote file:
- 33.4 MB
- SHA256:
- b6b09a0b4a803ad453063ca4bb49a784540e8120004e2450e025df2b27d41fb2
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