Instructions to use bilkultheek/ColdLLamaLite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use bilkultheek/ColdLLamaLite with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("ahxt/LiteLlama-460M-1T") model = PeftModel.from_pretrained(base_model, "bilkultheek/ColdLLamaLite") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25155433ad7ece5e028828e19d3b0ec3ae1ec30f3626210341e10021ee0e12a3
- Size of remote file:
- 19.7 MB
- SHA256:
- 8334fa9ce17038b2414505a8ab1b8bee86712df9f453e5b1bfbab728eb30131b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.