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:
- 613cc50b31ec0b89f6f2c00d6d66c92a8e232fc7ea2a6ae9c2c94b3357e69d77
- Size of remote file:
- 168 MB
- SHA256:
- 0998454fb96b99db6c59c3ea8ca6fa91e10494a0df344bd9dccec125265e8dd8
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