Instructions to use james92/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use james92/lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "james92/lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e82ae1c9a959e071d19aa4f5afd952b45a1948c6bbb14a0c00e5744032942575
- Size of remote file:
- 4.66 kB
- SHA256:
- 621ff14e95b5620fce8fe1cf605259e115aabbc0c3b5a92d983b045b424f002e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.