# Diabetica-7B

Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management

## Introduction Hello! Welcome to the huggingface repository for [Diabetica](https://arxiv.org/pdf/2409.13191). Our study introduced a reproducible framework for developing a specialized LLM capable of handling various diabetes tasks. We present three key contributions: - High-performance domain-specific model: Compared with previous generic LLMs, our model Diabetica, showed superior performance across a broad range of diabetes-related tasks, including diagnosis, treatment recommendations, medication management, lifestyle advice, patient education, and so on. - Reproducible framework: We offered a detailed method for creating specialized medical LLMs using open-source models, curated disease-specific datasets, and fine-tuning techniques. This approach can be adapted to other medical fields, potentially accelerating AI-assisted care development. - Comprehensive evaluation: We designed comprehensive benchmarks and conducted clinical trials to validate the model's effectiveness in clinical applications. This ensured our model's practical utility and sets a new standard for evaluating AI tools in diabetes care. Please refer to our [GitHub Repo](https://github.com/waltonfuture/Diabetica) for more details. ## Model Inference ```bash from transformers import AutoModelForCausalLM, AutoTokenizer import torch device = "cuda" # the device to load the model onto model_path = 'WaltonFuture/Diabetica-7B' model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_path) def model_output(content): messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": content} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=2048, do_sample=True, ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] return response prompt = "Hello! Please tell me something about diabetes." response = model_output(prompt) print(response) ``` ## Citation ``` @article{wei2024adapted, title={An adapted large language model facilitates multiple medical tasks in diabetes care}, author={Wei, Lai and Ying, Zhen and He, Muyang and Chen, Yutong and Yang, Qian and Hong, Yanzhe and Lu, Jiaping and Li, Xiaoying and Huang, Weiran and Chen, Ying}, journal={arXiv preprint arXiv:2409.13191}, year={2024} } ```