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
Download adapter_model.safetensors from windmaple/gemma-chinese: direct link, hf CLI and curl.
- Browser
- Download file 5.45 GB
-
https://huggingface.co/windmaple/gemma-chinese/resolve/cad09ca6a8b5480a7aa2883aeba56f77fd8b1208/adapter_model.safetensors
- Command line
-
hf download hf://windmaple/gemma-chinese@cad09ca6a8b5480a7aa2883aeba56f77fd8b1208/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/windmaple/gemma-chinese/resolve/cad09ca6a8b5480a7aa2883aeba56f77fd8b1208/adapter_model.safetensors
5.45 GB
- Xet hash:
- 5bc41e4bb4f46a8152364cdac2f6d6614efbc2ad4cc1d66b5e3f0b9743b3a42d
- Size of remote file:
- 5.45 GB
- SHA256:
- 9c2ef38a93a32767ed06b97c80e7815119b12a79d7f9509d41561690d1603691
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