Instructions to use mergisi/final_results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mergisi/final_results with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/flan-t5-large") model = PeftModel.from_pretrained(base_model, "mergisi/final_results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56d651ce97407e89864502184be5eaf0b8fe62582ba8ea8e810136a275282762
- Size of remote file:
- 4.86 kB
- SHA256:
- c519d9037b0deff3ac7817c724abe912eb6f9e81f905a3e61ca87179fa4c89ea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.