Instructions to use rayonlabs/MMed-Llama-3-8B-EnIns-PubMedQA-7bf07ae5-3245-4ab1-acb1-d42f13d74ddc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rayonlabs/MMed-Llama-3-8B-EnIns-PubMedQA-7bf07ae5-3245-4ab1-acb1-d42f13d74ddc with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Henrychur/MMed-Llama-3-8B-EnIns") model = PeftModel.from_pretrained(base_model, "rayonlabs/MMed-Llama-3-8B-EnIns-PubMedQA-7bf07ae5-3245-4ab1-acb1-d42f13d74ddc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0877fe99629f951cb687788e74f99782da0dcff4020ec052a86b51b7d2490dea
- Size of remote file:
- 14.2 kB
- SHA256:
- 9ee1b6ce7ca98a4ed47e849a52d9f1fb305f8ad281416c2f7521263e5525c124
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