Instructions to use abvijaykumar/bloom-560m-prefix-tuned-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abvijaykumar/bloom-560m-prefix-tuned-qa with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m") model = PeftModel.from_pretrained(base_model, "abvijaykumar/bloom-560m-prefix-tuned-qa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77a9bdb08ceb0f39a8e01fc9e0263fc0e04bd5f4008335e89368e37c66d8ccc5
- Size of remote file:
- 3.93 MB
- SHA256:
- 239c892036864edb7a82f1d0ad17022d21fc80ea707f995185fd382ec0a129a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.