Instructions to use fasterinnerlooper/llama-7b-qlora-csharp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fasterinnerlooper/llama-7b-qlora-csharp with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("stabilityai/stable-code-3b") model = PeftModel.from_pretrained(base_model, "fasterinnerlooper/llama-7b-qlora-csharp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c26bd5b3b20106a933bd625734bf0b84f37792ed0e6c60d22259d842fed576f
- Size of remote file:
- 4.79 kB
- SHA256:
- e346cf50a5b0bee9b165d7f02e3d138e373d62444cbfbaf705b7496686b84de1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.