OASISCoder-Llama-3.2-3B is a fine-tuned version of the LLaMA 3.2 3B model, trained specifically on a medical coding dataset focusing on CMS (Centers for Medicare & Medicaid Services) and OASIS (Outcome and Assessment Information Set) standards. The model is designed to assist healthcare professionals and organizations in generating accurate medical codes and streamlining documentation tasks. It supports medical question-answering, medical coding, and clinical decision support, with an emphasis on regulatory compliance and documentation quality in the US healthcare system.
The model was fine-tuned on a comprehensive medical coding dataset integrating CMS and OASIS data, including real-world clinical documentation and coding tasks. The dataset includes examples of medical diagnoses, procedures, patient assessments, and coding annotations following CMS and OASIS regulations.
The model is based on LLaMA 3.2 3B, a powerful large language model architecture optimized for language understanding and generation tasks. Fine-tuning on the medical domain allows it to provide highly specialized and accurate outputs for healthcare tasks.
The model is released under the Apache License 2.0, making it available for non-commercial research and development purposes.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("exafluence/OASISCoder-Llama-3.2-3B")
model = AutoModelForCausalLM.from_pretrained("exafluence/OASISCoder-Llama-3.2-3B")
input_text = "What is the CMS code for a patient with diabetes?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
If you use this model, please cite:
@inproceedings{exafluence2024OASISCoder,
title={OASISCoder-Llama-3.2-3B: A Medical Coding Language Model for CMS and OASIS},
author={Exafluence Inc.},
year={2024},
doi={10.57967/hf/3260},
url={https://huggingface.co/exafluence/OASISCoder-Llama-3.2-3B}
}
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
