Instructions to use HugThang/Fine-tuned-Zephyr7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HugThang/Fine-tuned-Zephyr7B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-beta-GPTQ") model = PeftModel.from_pretrained(base_model, "HugThang/Fine-tuned-Zephyr7B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 400360ad98885e3f1719ae7e1f0115eabb505d7033d6d5e7bfe82444dab28eef
- Size of remote file:
- 4.98 kB
- SHA256:
- 991ff07cfb42a792f18759d52870d51aeffad8c61f2bb43a11eff5b5f68e3628
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.