JIN HYUN KIM

Ophtimus: Ophthalmology-specific LLM

🤗 Models and Datasets  |  📕 AAAI 2025 workshop Paper

Introduction

Ophtimus is an open-source large language model (LLM) specialized in ophthalmology, built with 8 billion parameters based on the LLaMA architecture. It was trained on carefully curated ophthalmology-specific data, including medical papers, textbooks, and research reports. Through filtering, summarization, and preprocessing, only the most relevant and high-quality information was retained.

Designed to be both lightweight and high-performing, Ophtimus is suitable for real-world applications such as clinical decision support, medical education, and patient communication. The model and its training pipeline are fully open-sourced, providing a practical reference for developing similar domain-specific LLMs in other areas of medicine.

GitHub Repository: github.com/jinkimh/Ophtimus-Ophthalmology-LLM

Dataset Details

Model Details

Performance

Quickstart

Install Dependencies

cd Ophtimus-Ophthalmology-LLM
pip install -r requirements.txt

Ophtimus Inference

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# model_name example : BaekSeungJu/Ophtimus-Instruct-8B or Ophtimus-Llama-1B or Ophtimus-Llama-3B or Ophtimus-Llama-8B
model_name = "BaekSeungJu/Ophtimus-Instruct-8B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).to("cuda")

tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token

system_instruction = (
    "You are an expert ophthalmologist. Please provide accurate and "
    "medically sound answers to the user's ophthalmology-related question."
)

# Enter your questions in the list
questions = [
    "Please describe the symptoms and treatment of epiretinal membrane.",
    "What's good for eyes?"
]

prompts = []
for question in questions:
    row_json = [
        {"role": "system", "content": system_instruction},
        {"role": "user", "content": question}
    ]
    prompt = tokenizer.apply_chat_template(row_json, add_generation_prompt=True, tokenize=False)
    prompts.append(prompt)

input_ids = tokenizer(
    prompts,
    padding=True,
    return_tensors="pt",
)["input_ids"].to("cuda")

model.eval()
with torch.no_grad():
    outputs = model.generate(
        input_ids,
        max_new_tokens=1024,
        do_sample=False,
    )

decoded = tokenizer.batch_decode(outputs, skip_special_tokens=False)
for i, text in enumerate(decoded):
    print(f"------------------------\nAnswer for question {i+1}:\n{text}")

For more details, visit the GitHub repository.