Instructions to use Kubermatic/DeepCNCF2BAdapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Kubermatic/DeepCNCF2BAdapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it") model = PeftModel.from_pretrained(base_model, "Kubermatic/DeepCNCF2BAdapter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2996e167b3ee1aee61f280cae062effe38a3c5b5fa41952de8a52cd5cdc714b7
- Size of remote file:
- 5.18 kB
- SHA256:
- e21cc3e22c92f5aa0ee3ae1aeca5b180c53bdfd1dbe489cc5ddc895014c298d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.