Instructions to use shahvatsalm/Vatsals_LLM_TextGeneration_marketing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shahvatsalm/Vatsals_LLM_TextGeneration_marketing with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b7") model = PeftModel.from_pretrained(base_model, "shahvatsalm/Vatsals_LLM_TextGeneration_marketing") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67fe95e5d656a9b92fe90a8b8dd0129b57ba0aed3c7e7879d05bcd02ca99f5e1
- Size of remote file:
- 12.6 MB
- SHA256:
- 3455b3bdde92c1190382ff3ac98bd7955442c63a660bc8de4a35b659d1c5eec1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.