Instructions to use jackhogan/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jackhogan/results with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "jackhogan/results") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8573ad7a02cd576eef136a81eedc276bb112b3dac93207b672e0018b7dc43475
- Size of remote file:
- 5.63 kB
- SHA256:
- 5dcb6bb7cc93b7eee6c769e84910c96f7fab8ae1fb7dac8f5d6e63d11e8fbe4d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.