Instructions to use maedehm02/code-gemma-Code-Instruct-Finetune-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maedehm02/code-gemma-Code-Instruct-Finetune-test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("maedehm02/code-gemma-Code-Instruct-Finetune-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from maedehm02/code-gemma-Code-Instruct-Finetune-test: direct link, hf CLI and curl.
- Browser
- Download file 3.35 GB
-
https://huggingface.co/maedehm02/code-gemma-Code-Instruct-Finetune-test/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://maedehm02/code-gemma-Code-Instruct-Finetune-test/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/maedehm02/code-gemma-Code-Instruct-Finetune-test/resolve/main/adapter_model.safetensors
3.35 GB
- Xet hash:
- c7448d38c9e6ce3084b65e62b303970255c155dca33768cb730aa4b16861b5d0
- Size of remote file:
- 3.35 GB
- SHA256:
- ab07bf583af4377c08e23887611ad78ebf9c693a3d38552b8ee7d48017c81687
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.