Update README.md

README.md

@@ -14,7 +14,7 @@
 This is the PyLlama3 model, fine-tuned for <a href=" https://github.com/Mercury7353/PyBench" > PyBench </a>.  
 
 PyBench is a comprehensive benchmark evaluating LLM on real-world coding tasks including **chart analysis**, **text analysis**, **image/ audio editing**, **complex math** and **software/website development**.  
- We collect files from Kaggle, arXiv, and other sources and automatically generate queries according to the type and content of each file.  
+ We collect files from Kaggle, arXiv, and other sources and automatically generate queries according to the type and content of each file. As for evaluation, we design unit tests for each tasks. 
 
 ![Overview](images/main.png)   
 
@@ -24,10 +24,10 @@ PyBench is a comprehensive benchmark evaluating LLM on real-world coding tasks i
 ## Why PyBench?
 
 The LLM Agent, equipped with a code interpreter, is capable of automatically solving real-world coding tasks, such as data analysis and image processing.
-%
+
 However, existing benchmarks primarily focus on either simplistic tasks, such as completing a few lines of code, or on extremely complex and specific tasks at the repository level, neither of which are representative of various daily coding tasks. 
-%
-To address this gap, we introduce **PyBench**, a benchmark that encompasses 6 main categories of real-world tasks, covering more than 10 types of files. 
+
+To address this gap, we introduce **PyBench**, a benchmark that encompasses 5 main categories of real-world tasks, covering more than 10 types of files. 
 ![How PyBench Works](images/generateTraj.png)   
 
 ## 📁 PyInstruct
@@ -45,41 +45,7 @@ We trained Llama3-8B-base on PyInstruct, CodeActInstruct, CodeFeedback, and Jupy
 ## 🚀 Model Evaluation with PyBench! 
 <video src="https://github.com/Mercury7353/PyBench/assets/103104011/fef85310-55a3-4ee8-a441-612e7dbbaaab"> </video> 
 *Demonstration of the chat interface.* 
-### Environment Setup: 
-Begin by establishing the required environment:   
-
-```bash 
-conda env create -f environment.yml
-```   
-
-### Model Configuration 
-Initialize a local server using the vllm framework, which defaults to port "8001":     
-
-```bash 
-bash SetUpModel.sh
-```   
-
-
-A Jinja template is necessary to launch a vllm server. Commonly used templates can be located in the `./jinja/` directory.  
-Prior to starting the vllm server, specify the model path and Jinja template path in `SetUpModel.sh`.     
-### Configuration Adjustments   
-Specify your model's path and the server port in `./config/model.yaml`. This configuration file also allows for customization of the system prompts.  
-### Execution on PyBench  
-Ensure to update the output trajectory file path in the script before execution:    
-
-```bash 
-python /data/zyl7353/codeinterpreterbenchmark/inference.py --config_path ./config/<your config>.yaml --task_path ./data/meta/task.json --output_path <your trajectory.jsonl path>
-```  
-
-
-### Unit Testing Procedure   
-- **Step 1:** Store the output files in `./output`.
-- **Step 2:** Define the trajectory file path in
-  `./data/unit_test/enter_point.py`.
-- **Step 3:** Execute the unit test script: 
-  ```bash
-  python data/unit_test/enter_point.py  
-  ```
+- Detailed in <a href=" https://github.com/Mercury7353/PyBench" > 🚗Github </a>
   
 ## 📊 LeaderBoard 
 ![LLM Leaderboard](images/leaderboard.png)