Text Generation
PEFT
Safetensors
English
medical
radiology
summarization
clinical-nlp
healthcare
lora
medphi
impression-generation
Eval Results (legacy)
Instructions to use sabber/medphi-radiology-summary-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sabber/medphi-radiology-summary-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/MediPhi-Instruct") model = PeftModel.from_pretrained(base_model, "sabber/medphi-radiology-summary-adapter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b694518e9e3ff426e3cb97eb4fa7feab1546430e056c7f5350352f93a27d703f
- Size of remote file:
- 50.4 MB
- SHA256:
- ea3d3d1e4d64d7f80eb410b233ee06333f235747f0cd7cca55174c3cb94bface
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