---
license: apache-2.0
language:
- zh
library_name: transformers
pipeline_tag: text-generation
tags:
- Doctor_consultation
- Taiwan
- fine-tuning
- medicine
---

# 🔎 Taiwan-inquiry_7B_v_2.1

<!-- Provide a quick summary of what the model is/does. -->

"The model was fine-tuned based on the **Breeze-7B-Instruct-v1_0** model using a dataset that includes 614 authentic dialogues from the National Cheng Kung University Hospital. 
Additionally, 336 synthetic dialogues were included in the training set, carefully crafted to encompass themes drawn from sample questions of the OSCE (臨床技能測驗) sample questions in Taiwan. 
These synthetic dialogues were generated using GPT-3.5, Geminio-Pro and Breexe-8x7B-Instruct-v0_1. 
The training process was geared towards simulating verbal exchanges between doctors and patients within a hospital environment."

<img src="https://cdn-uploads.huggingface.co/production/uploads/65c07d1b2357c1bded7a92fa/e7QbiYh07kcGwyKniAo0e.png" alt="image/png" style="width:80%; height:auto;">

**************************** **Updates** ****************************
* 2024/04/25 🎉 Released [Taiwan-inquiry_7B_v2.1-awq](https://huggingface.co/ChenWeiLi/Taiwan-inquiry_7B_v2.1-awq)
* 2024/04/29 🎉 Released [Taiwan-inquiry_7B_v2.1.gguf](https://huggingface.co/ChenWeiLi/Taiwan-inquiry_7B_v2.1.gguf)
 
### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [Joseph (Chen-Wei) Li](https://www.linkedin.com/in/joseph-li-3a453b231/), researcher assistant from National Taiwan University Hospital.
- **Model type:**  A 7B parameter GPT-like model fine-tuned on a combination of private and synthetic dialogue datasets.
- **Language(s) (NLP):** Traditional Chinese (zh-tw)
- **Finetuned from model :** [Breeze-7B-Instruct-v1_0 ](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0)

### Usage of the model
- The user can take on the role of a doctor, and the model can engage in conversation with you as if it were a patient.
- You can provide the model with a brief patient background in the system prompt, and the model will respond based on that prompt. **(using my patient generator: [**colab**](https://colab.research.google.com/drive/17MSob_tQ2hPtMBL0xOF2zzV6WWe4dEG6?usp=sharing))**
- Directly asking the certain disease about the symptoms and the possible therapies.**（Warning: It's not medical advice!)** 

### Model evaluation
The model got the **TMMLU+** (0 shot) performance using [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) with default settings.

|Details on TMMLU+ (0 shot):<br/>Model                |       Base Model      |      STEM       | Social Science | Humanities | Other      |   AVG   |
|-----------------------------------------------------|:---------------------:|:---------------:|:--------------:|:----------:|:----------:|:-------:|
| Taiwan-inquiry_7B_v2.1                              |Breeze-7B-Instruct-v1_0| 36.06           | 44.61          | 37.49      | 39.61      | 40.29   |
| Taiwan-inquiry_7B_v2.0                              |Breeze-7B-Instruct-v0_1| 36.17           | 43.59          | 35.45      | 37.63      | 38.95   |
| Taiwan-inquiry_7B_v1.0                              |Taiwan-LLM-7B-v2.1-chat| 26.74           | 29.47          | 26.83      | 29.61      | 28.83   |

### DEMO


![image/png](https://cdn-uploads.huggingface.co/production/uploads/65c07d1b2357c1bded7a92fa/hZpp2SGb0iBYdLJvFvDqZ.png)
![image/png](https://cdn-uploads.huggingface.co/production/uploads/65c07d1b2357c1bded7a92fa/KIKYt8xfLOz9bhiUYzS2Q.png)