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:
- 0c10f55df37e1cb25040bb904699c56600e97ebb57b5488491c29b655b7efb93
- Size of remote file:
- 336 MB
- SHA256:
- 5e3600869dbed83b209ef9d2eda37333c11c4aecbb2378e8cfd1daf2e7e95179
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