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:
- 8578e2fd702a2b80d25395850010f88fde48ece2b75471472e2acd3cfe877bb3
- Size of remote file:
- 5.45 GB
- SHA256:
- bca4434a4bb580952fea81815accab3e1fc54917b72cf95d48abfc8b483f9255
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.