Instructions to use nblinh63/e0779520-1b67-4e5a-b438-9775d5fdd111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/e0779520-1b67-4e5a-b438-9775d5fdd111 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/OpenHermes-2.5-Mistral-7B") model = PeftModel.from_pretrained(base_model, "nblinh63/e0779520-1b67-4e5a-b438-9775d5fdd111") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c9cb2d6bc5eb1dd9a013cb09680d6ce01ebef7fdc5430980173c2733e9a6597
- Size of remote file:
- 168 MB
- SHA256:
- d21529a8ceee28d288099d5d5f66c4af23aa8dbf4ea88a2e4d3e44e5664bcc8f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.