Instructions to use nblinh63/100a0c0b-2d76-4902-8ff6-a4302c454c37 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nblinh63/100a0c0b-2d76-4902-8ff6-a4302c454c37 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Pro-Mistral-7B") model = PeftModel.from_pretrained(base_model, "nblinh63/100a0c0b-2d76-4902-8ff6-a4302c454c37") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b27325eb6db3c85567d3a3b39a0e581a74ef336def4057b82476f1ddc30bd85
- Size of remote file:
- 168 MB
- SHA256:
- f02eb4687853619a607eee9b2aa942f7f2302f60c6089ea10f12c4a0f3a7abf1
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