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:
- 5333b0c3e14a7b4d352548a39ca4782e3caf0a4448685bf6dc74ec768c537bb7
- Size of remote file:
- 6.84 kB
- SHA256:
- 6982f1638cfdf78edc007ffa0b07f401d37555e418843c42685040f12dae6f83
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.