Instructions to use adhisetiawan/dpo-mmed-rag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adhisetiawan/dpo-mmed-rag with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/llava-med-v1.5-mistral-7b") model = PeftModel.from_pretrained(base_model, "adhisetiawan/dpo-mmed-rag") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7268c3c9b055da0e7e27a5b8a7c4fa27e7de76f05cff6a37b6fc6691e68e7a68
- Size of remote file:
- 912 Bytes
- SHA256:
- 60fb82c3660319e6d0b239950b20c28181e97f1ade117dc0660b40e2ad94a89b
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