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:
- 68018427b9a3b4a16c17b73e9c7475fa51c8ad219d88e81ba21fbcbef11c1b8c
- Size of remote file:
- 168 MB
- SHA256:
- 8b5574b2929f794cb3e3f197d0d48d81161c26f106df84e397274daa9d9caaeb
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