Instructions to use nblinh63/60289eab-d34e-4163-91bd-8913eca9f86a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/60289eab-d34e-4163-91bd-8913eca9f86a with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("rayonlabs/e28c0e27-22e9-48ef-a9b8-18433a6bac9d") model = PeftModel.from_pretrained(base_model, "nblinh63/60289eab-d34e-4163-91bd-8913eca9f86a") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86e9fd8aef05348b8e3c102716dc67e25bba70673293262db0702bac01e8bb20
- Size of remote file:
- 6.78 kB
- SHA256:
- 3c4dedaa389ecc6c9631a48d448b17cc3765ccba411045fe33a56bb06fc35ec2
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