Instructions to use demohong/7d91a01e-c9c6-4792-8a1d-bc0ded2a876e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use demohong/7d91a01e-c9c6-4792-8a1d-bc0ded2a876e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/codegemma-2b") model = PeftModel.from_pretrained(base_model, "demohong/7d91a01e-c9c6-4792-8a1d-bc0ded2a876e") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46452bfc23c4816dd8bd1e7cc10c59685742f1a62b11a7c67f6ef1c25c98c5c2
- Size of remote file:
- 6.78 kB
- SHA256:
- 5245adcdaa893e49207d5c77fca7f420c722b1fbfb955a8bc72270f73b41e3a5
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