Instructions to use quannh197/f5fb298c-df1a-4966-9c77-a5b493faeff6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use quannh197/f5fb298c-df1a-4966-9c77-a5b493faeff6 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("sethuiyer/Medichat-Llama3-8B") model = PeftModel.from_pretrained(base_model, "quannh197/f5fb298c-df1a-4966-9c77-a5b493faeff6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5c906e07ef2cca9ac73aa7d2a08081c0ad9f1d6dfef00e8d32339927a57454cd
- Size of remote file:
- 84 MB
- SHA256:
- 90ddca2a74a6c3c0cf72ca35df52bd8eb85e7f7028dbee28667433bee4f4413e
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