fine-tuned-openbiollm-medical-coding

Fine-tuned version of aaditya/Llama3-OpenBioLLM-8B for automated ICD medical coding from clinical text. This model extends OpenBioLLM's strong biomedical language understanding with task-specific fine-tuning on ICD-10 code assignment.

Model Description

This model was developed as part of a research effort to evaluate multiple biomedical LLMs on the medical coding task. OpenBioLLM-8B provides a strong foundation in biomedical language understanding (pre-trained on PubMed, clinical notes, and biomedical corpora), and this fine-tune further specializes it for structured ICD-10 output from unstructured clinical text.

• Base model: aaditya/Llama3-OpenBioLLM-8B
• Fine-tuning method: SFT (Supervised Fine-Tuning) via TRL
• Task: ICD-10 code generation from clinical text
• Domain: Clinical NLP / Healthcare AI
• Parameters: ~8B

Intended Uses

• Automated medical coding assistance in clinical documentation workflows
• Research benchmarking of biomedical LLMs on ICD coding tasks
• Integration into clinical decision support pipelines (with human oversight)

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "abnuel/fine-tuned-openbiollm-medical-coding"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = """You are a clinical coding assistant. Given the following clinical note, 
provide the most appropriate ICD-10 code(s).

Clinical note: Patient diagnosed with essential hypertension and stage 2 chronic kidney disease.

ICD-10 Code(s):"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

• Fine-tuning framework: TRL (Transformer Reinforcement Learning)
• Method: Supervised Fine-Tuning (SFT)
• Base model: Llama3-OpenBioLLM-8B (biomedical-specialized Llama 3)
• Hardware: GPU (CUDA)

Limitations

• As with all LLM-based coding tools, outputs should be reviewed by a certified medical coder before use in billing or clinical records.
• May not generalize to all ICD-10-CM editions, regional coding conventions, or highly specialized subspecialties.
• The model does not have access to real-time coding updates or payer-specific guidelines.

Related Models & Resources

• abnuel/MedGemma-4b-ICD — MedGemma-4b fine-tuned on the same task
• abnuel/MedGemma-4b-ICD-Coder — companion checkpoint
• 🚀 Live demo: spaces/abnuel/med-coding

Citation

@misc{adegunlehin2025openbiollm-coding,
  author = {Abayomi Adegunlehin},
  title  = {Fine-tuned OpenBioLLM-8B for ICD-10 Medical Coding},
  year   = {2025},
  url    = {https://huggingface.co/abnuel/fine-tuned-openbiollm-medical-coding}
}