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:
- 9efa0927b029e0893ad519e3b63b567dc0c206f523d78c2043ad1f0d6f3ef585
- Size of remote file:
- 314 MB
- SHA256:
- fc9d38871e319c008e324db17f82987be71bdc9ef15593eaa05034c4c6ec813f
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