Instructions to use abaddon182/4211dd7b-7323-4f98-88a0-2c950d914fad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use abaddon182/4211dd7b-7323-4f98-88a0-2c950d914fad with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("teknium/OpenHermes-2.5-Mistral-7B") model = PeftModel.from_pretrained(base_model, "abaddon182/4211dd7b-7323-4f98-88a0-2c950d914fad") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 265ba186349949ec096b47c0a77e96f9af01155f1b316aaebf3230ba9f47bb75
- Size of remote file:
- 42.1 MB
- SHA256:
- c435ec08150c1cec86f9ce414fb5c146dffb1f8cc3da54f17f1b5b9be8038a57
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.