97 lines
3.3 KiB
Markdown
97 lines
3.3 KiB
Markdown
---
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library_name: transformers
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pipeline_tag: text-generation
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license: mit
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base_model:
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- Qwen/Qwen2-7B-Instruct
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tags:
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- medical
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datasets:
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- WaltonFuture/Diabetica-SFT
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---
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# Diabetica-7B
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<div align="center">
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<h2>
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Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management
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</h2>
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</div>
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<p align="center">
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<a href="https://github.com/waltonfuture/Diabetica" target="_blank">Code</a>｜<a href="https://arxiv.org/pdf/2409.13191" target="_blank">Paper</a> | <a href="https://huggingface.co/datasets/WaltonFuture/Diabetica-SFT" target="_blank">Dataset</a><br>
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</p>
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## Introduction
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Hello! Welcome to the huggingface repository for [Diabetica](https://arxiv.org/pdf/2409.13191).
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Our study introduced a reproducible framework for developing a specialized LLM capable of handling various diabetes tasks. We present three key contributions:
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- 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.
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- 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.
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- 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.
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Please refer to our [GitHub Repo](https://github.com/waltonfuture/Diabetica) for more details.
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## Model Inference
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```bash
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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device = "cuda" # the device to load the model onto
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model_path = 'WaltonFuture/Diabetica-7B'
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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def model_output(content):
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": content}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=2048,
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do_sample=True,
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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prompt = "Hello! Please tell me something about diabetes."
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response = model_output(prompt)
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print(response)
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```
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## Citation
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```
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@article{wei2024adapted,
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title={An adapted large language model facilitates multiple medical tasks in diabetes care},
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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},
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journal={arXiv preprint arXiv:2409.13191},
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year={2024}
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}
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``` |