File size: 3,208 Bytes
d07bd88 e0f22b9 d07bd88 788cf6b d07bd88 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
---
license: mit
base_model:
- Qwen/Qwen2-1.5B-Instruct
tags:
- medical
---
# Diabetica-1.5B
<div align="center">
<h2>
An adapted large language model facilitates multiple medical tasks in diabetes care
</h2>
</div>
<p align="center">
<a href="https://github.com/waltonfuture/Diabetica" target="_blank">Code</a> ｜<a href="https://arxiv.org/pdf/2409.13191" target="_blank">Paper</a> <br>
</p>
## 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-1.5B'
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}
}
```
|