初始化项目，由ModelHub XC社区提供模型

Model: WaltonFuture/Diabetica-7B
Source: Original Platform

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README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+license: mit
+base_model:
+- Qwen/Qwen2-7B-Instruct
+tags:
+- medical
+datasets:
+- WaltonFuture/Diabetica-SFT
+---
+
+# Diabetica-7B
+
+<div align="center">
+<h2>
+    Diabetica: Adapting Large Language Model to Enhance Multiple Medical Tasks in Diabetes Care and Management
+</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>  |  <a href="https://huggingface.co/datasets/WaltonFuture/Diabetica-SFT" target="_blank">Dataset</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
+```
+@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}
+}
+```

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+  ],
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+  "vocab_size": 152064
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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