Diabetica-7B Open-Source Large Language Model - Free Support for Multiple Medical Tasks in Diabetes Care Management

Diabetica 7B

Developed by WaltonFuture

Diabetica is a large language model designed specifically for diabetes care and management, capable of handling various medical tasks such as diagnosis, treatment advice, and medication management.

Large Language Model

Transformers

Open Source License:MIT #Diabetes specialty #Medical decision support #Clinical text generation

Downloads 120

Release Time : 8/16/2024

Model Overview

This model focuses on diabetes-related medical tasks by fine-tuning an open-source large language model, providing high-performance solutions in specific domains.

Model Features

High-performance specific domain model

Performs excellently in diabetes-related tasks, including diagnosis, treatment advice, and medication management.

Reproducible framework

Provides detailed methods to create a professional medical large language model using open-source models and specific disease datasets.

Comprehensive evaluation

Validates the effectiveness of the model in clinical applications through designed benchmark tests and clinical trials.

Model Capabilities

Diabetes diagnosis

Treatment advice generation

Medication management advice

Lifestyle advice

Patient education

Use Cases

Medical care

Diabetes diagnosis assistance

Assists doctors in diabetes diagnosis and provides relevant advice.

Improves diagnosis efficiency and accuracy

Patient education

Provides lifestyle and diet advice for diabetes patients.

Enhances patients' self-management ability

🚀 Diabetica-7B

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

This project presents a specialized large language model (LLM) for diabetes care, offering high - performance in various diabetes - related tasks, a reproducible framework for development, and comprehensive evaluation methods.

🚀 Quick Start

Hello! Welcome to the Hugging Face repository for Diabetica.

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 for more details.

✨ Features

Domain - specific Excellence: Delivers outstanding performance in multiple diabetes - related tasks.
Reproducible Design: A framework that can be replicated for other medical fields.
Comprehensive Validation: Rigorous benchmarks and clinical trials for real - world applicability.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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)

📄 License

This project is licensed under the MIT license.

📚 Documentation

Model Information

Property	Details
Library Name	transformers
Pipeline Tag	text - generation
Model Type	Diabetica - 7B, based on Qwen/Qwen2 - 7B - Instruct
Training Data	WaltonFuture/Diabetica - SFT
License	MIT

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}
}