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:
- 4b44026d0f0d50cd6f62bc6641c57ec5e4c27c49938cbb494dc744febf2bc44e
- Size of remote file:
- 43.1 MB
- SHA256:
- bb8eebcaf2ba29aa3f2d98c42155261e2cb0981da63883bf5a16602fde23e011
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