from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("hugo/protocolos-clinicos-br-cpt-1gen-14b", torch_dtype="auto", device_map="auto")
tok  = AutoTokenizer.from_pretrained("hugo/protocolos-clinicos-br-rl-1gen-14b")
model = PeftModel.from_pretrained(base, "hugo/protocolos-clinicos-br-rl-1gen-14b")

Intended use & limitations

Base model: <a href="https://huggingface.co/hugo/protocolos-clinicos-br-cpt-1gen-14b" target="_blank" rel="noopener noreferrer" class="text-blue-400 hover:text-blue-300 underline underline-offset-2 transition-colors">hugo/protocolos-clinicos-br-cpt-1gen-14b
Type: LoRA adapter (PEFT)

Research model for studying domain adaptation of LLMs to Brazilian clinical guidelines. Not a certified medical device. Even at the best accuracy reported in the paper, residual errors may involve consequential details (dosages, contraindications). Use only under qualified professional supervision.

Citation

See the paper and code at the project repository:

Code & paper: https://github.com/hugoabonizio/clinical-protocols-br