Instructions to use tripathiarpan20/tuning-ab3318ee-d929-45d5-97e1-ccfee77df372 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tripathiarpan20/tuning-ab3318ee-d929-45d5-97e1-ccfee77df372 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "tripathiarpan20/tuning-ab3318ee-d929-45d5-97e1-ccfee77df372") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77f50cb6bda67d259b520eaea4f8f8fc95a36420a28742f930349343344e4bd7
- Size of remote file:
- 1.06 kB
- SHA256:
- bb578e75c11a81e85dda67a691f96ba4793a02960f1409fd3e1511aac873491a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.