初始化项目，由ModelHub XC社区提供模型

Model: medalpaca/medalpaca-7b
Source: Original Platform

README.md

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+---
+license: cc
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- medical
+---
+# MedAlpaca 7b
+
+
+## Table of Contents
+
+[Model Description](#model-description)  
+- [Architecture](#architecture)    
+- [Training Data](#trainig-data)  
+[Model Usage](#model-usage)  
+[Limitations](#limitations)  
+
+## Model Description
+### Architecture
+`medalpaca-7b` is a large language model specifically fine-tuned for medical domain tasks. 
+It is based on LLaMA (Large Language Model Meta AI) and contains 7 billion parameters. 
+The primary goal of this model is to improve question-answering and medical dialogue tasks.
+Architecture
+
+
+### Training Data
+The training data for this project was sourced from various resources. 
+Firstly, we used Anki flashcards to automatically generate questions, 
+from the front of the cards and anwers from the back of the card. 
+Secondly, we generated medical question-answer pairs from [Wikidoc](https://www.wikidoc.org/index.php/Main_Page). 
+We extracted paragraphs with relevant headings, and used Chat-GPT 3.5 
+to generate questions from the headings and using the corresponding paragraphs 
+as answers. This dataset is still under development and we believe 
+that approximately 70% of these question answer pairs are factual correct. 
+Thirdly, we used StackExchange to extract question-answer pairs, taking the 
+top-rated question from five categories: Academia, Bioinformatics, Biology, 
+Fitness, and Health. Additionally, we used a dataset from [ChatDoctor](https://arxiv.org/abs/2303.14070) 
+consisting of 200,000 question-answer pairs, available at https://github.com/Kent0n-Li/ChatDoctor.
+
+| Source                      | n items |
+|------------------------------|--------|
+| ChatDoc large                | 200000 |
+| wikidoc                      | 67704  |
+| Stackexchange academia       | 40865  |
+| Anki flashcards              | 33955  |
+| Stackexchange biology        | 27887  |
+| Stackexchange fitness        | 9833   |
+| Stackexchange health         | 7721   |
+| Wikidoc patient information  | 5942   |
+| Stackexchange bioinformatics | 5407   |
+
+## Model Usage
+To evaluate the performance of the model on a specific dataset, you can use the Hugging Face Transformers library's built-in evaluation scripts. Please refer to the evaluation guide for more information.
+Inference
+
+You can use the model for inference tasks like question-answering and medical dialogues using the Hugging Face Transformers library. Here's an example of how to use the model for a question-answering task:
+
+```python
+
+from transformers import pipeline
+
+pl = pipeline("text-generation", model="medalpaca/medalpaca-7b", tokenizer="medalpaca/medalpaca-7b")
+question = "What are the symptoms of diabetes?"
+context = "Diabetes is a metabolic disease that causes high blood sugar. The symptoms include increased thirst, frequent urination, and unexplained weight loss."
+answer = pl(f"Context: {context}\n\nQuestion: {question}\n\nAnswer: ")
+print(answer)
+```
+
+## Limitations
+The model may not perform effectively outside the scope of the medical domain.
+The training data primarily targets the knowledge level of medical students, 
+which may result in limitations when addressing the needs of board-certified physicians.
+The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown. 
+It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_medalpaca__medalpaca-7b)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 44.98   |
+| ARC (25-shot)         | 54.1          |
+| HellaSwag (10-shot)   | 80.42    |
+| MMLU (5-shot)         | 41.47         |
+| TruthfulQA (0-shot)   | 40.46   |
+| Winogrande (5-shot)   | 71.19   |
+| GSM8K (5-shot)        | 3.03        |
+| DROP (3-shot)         | 24.21         |