Instructions to use melsiddieg/gemma3-4b-lora-arud-aren-16bit-1.3k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use melsiddieg/gemma3-4b-lora-arud-aren-16bit-1.3k with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("melsiddieg/gemma3-4b-lora-arud-aren-16bit-1.3k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
- Xet hash:
- 342f2a98c9304f312b0fffb9c78c3fadf7847bcc909dd1fa9d00be5183cc5dee
- Size of remote file:
- 3.64 GB
- SHA256:
- 972e7c8c6a9fc0c9fba4ace9ccaa5e88d829607a97412c5d785b773d4351f11c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.