Instructions to use scuba-diver93/gemma_2b_chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use scuba-diver93/gemma_2b_chinese with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("model/gemma_2b") model = PeftModel.from_pretrained(base_model, "scuba-diver93/gemma_2b_chinese") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a33bd44e65a5a42491dee9ac9e848ea92b12fde5a06672c28f8bdbcdfd1f8016
- Size of remote file:
- 17.5 MB
- SHA256:
- 376f7d962ebaed8fbd7b003204ab85d5eaaf08c3515aa607b0252255fa9160a1
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