Instructions to use jacksnacks/second_qlora_model_xgen_inst_faq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jacksnacks/second_qlora_model_xgen_inst_faq with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Salesforce/xgen-7b-8k-inst") model = PeftModel.from_pretrained(base_model, "jacksnacks/second_qlora_model_xgen_inst_faq") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c965ad4af33c8b716c35f6b93d1c5f14f658310c1d62fe191cfd6cee74aeadd
- Size of remote file:
- 33.6 MB
- SHA256:
- c6a9b6170d8614a5ac430d477f59949980f83cb6258d4ccdf5f650e3e7eddca2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.