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:
- 349d83fbd7304e6b2046955cbbaf3e11796b4d4152dfd080024f1a6d5bbc68a7
- Size of remote file:
- 6.78 kB
- SHA256:
- 676f89be7f97b5c1579b09cdbb486dd9d6b39e42a4c4f1ccc72afaf496dd711e
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