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:
- 0645eb9b56172ca0880e982edb4275ef41f442ea3a0929d6e48aef7acc2db95a
- Size of remote file:
- 4.69 MB
- SHA256:
- aa009fcbc3589a9904d30d04834094fea4653c2ac6d2de2cd1262d4f7a50ceb3
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