Instructions to use mamung/29b84e13-2f1c-406f-9e7e-fd35b6e5ff49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mamung/29b84e13-2f1c-406f-9e7e-fd35b6e5ff49 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openlm-research/open_llama_3b") model = PeftModel.from_pretrained(base_model, "mamung/29b84e13-2f1c-406f-9e7e-fd35b6e5ff49") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfce7fd1423c5c35b2427cbc9db01dce5a4a271294b4b7b46abc20b662e33890
- Size of remote file:
- 50.9 MB
- SHA256:
- 335a4bc81100128d9ddb1835316fbf10434b7ee8f886860cd37a93fe7e3c942d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.