Instructions to use randomb0tt/lora-tagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use randomb0tt/lora-tagger with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1") model = PeftModel.from_pretrained(base_model, "randomb0tt/lora-tagger") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f880044c3186fa4edf5117404c746714cb1ab1742d26ceb5d3bcd3dde156d77
- Size of remote file:
- 21 MB
- SHA256:
- e7966037e6f72e40c220e17505fd59d0d7589b2b142da31a90ec9f5a95fb1e50
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.