Instructions to use prxy5608/2f902545-d43c-4287-8d9d-e2c26c57d440 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use prxy5608/2f902545-d43c-4287-8d9d-e2c26c57d440 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Xenova/tiny-random-Phi3ForCausalLM") model = PeftModel.from_pretrained(base_model, "prxy5608/2f902545-d43c-4287-8d9d-e2c26c57d440") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69a203fc7597582a5e34dae2b93fc84e15be72bc48f9bce3033760069495d9de
- Size of remote file:
- 231 kB
- SHA256:
- fb0da074a1d49f8435894e806a05265a617741d2c412ba32cac6037cec53a74f
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