Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+base_model:
+- Qwen/Qwen2-7B-Instruct
+tags:
+- medical
+---
+
+# Diabetica-7B
+
+<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-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
+```
+@misc{wei2024adaptedlargelanguagemodel,
+      title={An adapted large language model facilitates multiple medical tasks in diabetes care}, 
+      author={Lai Wei and Zhen Ying and Muyang He and Yutong Chen and Qian Yang and Yanzhe Hong and Jiaping Lu and Xiaoying Li and Weiran Huang and Ying Chen},
+      year={2024},
+      eprint={2409.13191},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.13191}, 
+}
+```
+