Instructions to use windmaple/gemma-chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use windmaple/gemma-chinese with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b") model = PeftModel.from_pretrained(base_model, "windmaple/gemma-chinese") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fce006b9ce9f89dad77ea47ad9b5b12dab879eb0ce3538e6cfc531d87ff3c4b9
- Size of remote file:
- 4.92 kB
- SHA256:
- 899ea2b742a1b3ec952c7e10b3c3eaa689049970f2923c2baf22debe28a88012
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.