Update README.md

README.md

@@ -12,21 +12,119 @@ base_model: meta-llama/Llama-2-7b-chat-hf
 instance_prompt: null
 license: mit
 ---
-# Llama-2-7b-chinese-chat
+<div align="center">
+<h1>
+  EyeDoc
+</h1>
+<h2>ophthalmic consultation foundation model</h2>
+</div>
+<p align="center">
+📝 <a href="https://arxiv.org/" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/sperfu/EyeDoc" target="_blank">Github</a>
+</p>
 
-<Gallery />
 
-## Model description 
 
-This model is designed to answer ophthalmology-related questions using domain-specific knowledge. It is based on a large language model architectureLlama-2-7b-chat-hf and fine-tuned on expert-curated question-answer (QA) pairs covering eye diseases such as glaucoma, cataracts, and retinal disorders.
 
-The model supports both direct question answering and retrieval-augmented generation (RAG). In RAG mode, the model first retrieves relevant knowledge snippets from a structured ophthalmic knowledge base and then generates a medically accurate answer grounded in those facts.
+## ✨ Recent News
 
-Role-specific prompts (e.g., “as a doctor” or “as a patient”) are also used to control the communication style and adapt the response tone accordingly. The model aims to support medical professionals and patients in understanding, explaining, or verifying ophthalmic conditions.
+- [6/21/2024] We have upgraded a web-based interface for users to use.
+- [2/18/2024] The first version of the model was released.
 
+## ⚡ Introduction
 
-## Download model
+EyeDoc is the first open-source large language model focused on ophthalmic diseases. Our goal in developing EyeDoc is to create a more specialized large language model for the specific medical consultation scenario of ophthalmic diseases. Overall, compared to other medical large language models, our contributions are as follows:
 
-Weights for this model are available in Safetensors,PyTorch format.
+1. We have collected over 40,000 single-turn QA dialogues and nearly 9,000 multi-turn dialogues related to ophthalmic diseases. To standardize the multi-turn dialogue data, we used **gpt-3.5-turbo** for data cleaning.
 
-[Download](/AI4Bread/Eyedoctor/tree/main) them in the Files & versions tab.
+2. We have gathered knowledge information on 519 common ophthalmic diseases and constructed a specialized knowledge base for auxiliary diagnosis of ophthalmic diseases.
+
+3. We have fully considered the knowledge differences and language characteristics of doctors and patients during consultations, and based on this, we separately represented the features for doctors and patients.
+
+![Model Pipeline](assets/img/eye_main.jpg)
+
+
+## 🤖 Installation
+
+Use conda to create a new virtual environment with Python 3.9.0. Run the following command:
+
+```
+git clone https://github.com/sperfu/EyeDoc
+conda create --name EyeDoc python=3.9.0
+conda activate EyeDoc
+pip install -r requirements.txt
+
+<!-- ```
+python==3.9.0
+torch==2.1.2
+transformers==4.35.2
+peft==0.7.1
+accelerate==0.25.0
+bitsandbytes==0.42.0
+rouge_chinese
+nltk
+``` -->
+
+## 💭 Preparation
+
+EyeDoc is fine-tuned based on a large language model. Before training, please configure or download the base large language model.
+
+| Parameter Scale | Large Language Model Name                                     |
+| ----------------| --------------------------------------------------------------|
+| 1B              | [TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) |
+| 3B              | [bloom-zh-3b-chat](https://huggingface.co/ikala/bloom-zh-3b-chat) |
+| 7B              | [Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) |
+
+## ⚒️ Training
+
+### 1. QA Training
+
+```python
+python A_train.py
+# For more model hyperparameter adjustments, see the main function
+```
+
+
+###  2.  Multi-turn Training
+
+```python
+python A_train_doc_specific.py
+# For more model hyperparameter adjustments, see the main function
+```
+
+##  🧐 Evaluation
+
+```python
+python A_evaluate.py
+# For more model hyperparameter adjustments, see the main function
+```
+
+## 🚀 Generate
+
+```
+python A_generate.py	# # Model response generation
+python A_Flask_web.py	# Model API interface call
+```
+
+## 🌐 Deployment
+
+To deploy the EyeDoc model using Streamlit, follow these steps:
+
+### 1. Install the required environment
+
+First, ensure you have Python installed (preferably Python 3.9) and a GPU with at least 48GB VRAM for optimal performance. Then, install Streamlit and other necessary packages:
+
+```bash
+pip install streamlit
+pip install -r requirements.txt  # Ensure all dependencies listed in the requirements file are installed 
+```
+
+### 2. Start the Streamlit service
+
+Run the Streamlit application using the following command:
+
+```bash
+streamlit run st_chat_login.py
+```
+### 2. Start the Streamlit service
+
+This will start the Streamlit service, and you can access the web interface through the provided local URL (e.g., http://localhost:8501).