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:
- 44c313b183b931c0cbe6ab671406bb169c46903db6109ecd32a91263ce6ba60e
- Size of remote file:
- 6.84 kB
- SHA256:
- 7325c572708045c1d6d7a424f8fabd6eb9318a351ea6392f7003396ee422da35
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