You are a helpful AI scientist to build up the codebase for me.
This project is to train the open-sourced model to deploy CoT-like reasoning format on text-to-image and text-to-video generation quality assessment. You are using the LLaMAFactory to train the model, and write evaluation functions.
Preparation
Data
There are a folder called ea-data/agent and there are 3 subfolders:
vbench_results: which stores the results for using proprietary models to evaluate different dimensions in vbench, and the results are CoT style.t2i_results: which stores the results for using proprietary models to evaluate different dimensions in T2I-CompBench, and the results are CoT style.open_results: which store the results for using proprietary models to evaluate open-ended queries.
Your first job is to write and execute the python script to clean the data in those aforementioned folders and convert them into the format align with /home/data2/sltian/code/evaluation_agent_dev/LLaMA-Factory/data/alpaca_en_demo.json.
如果指定， system 列对应的内容将被作为系统提示词。
history 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮对话的指令和回答。注意在指令监督微调时，历史消息中的回答内容也会被用于模型学习。
指令监督微调数据集 格式要求 如下：
[ { "instruction": "人类指令（必填）", "input": "人类输入（选填）", "output": "模型回答（必填）", "system": "系统提示词（选填）", "history": [ ["第一轮指令（选填）", "第一轮回答（选填）"], ["第二轮指令（选填）", "第二轮回答（选填）"] ] } ] 下面提供一个 alpaca 格式 多轮 对话的例子，对于单轮对话只需省略 history 列即可。
[ { "instruction": "今天的天气怎么样？", "input": "", "output": "今天的天气不错，是晴天。", "history": [ [ "今天会下雨吗？", "今天不会下雨，是个好天气。" ], [ "今天适合出去玩吗？", "非常适合，空气质量很好。" ] ] } ] 对于上述格式的数据， dataset_info.json 中的 数据集描述 应为：
"数据集名称": { "file_name": "data.json", "columns": { "prompt": "instruction", "query": "input", "response": "output", "system": "system", "history": "history" } }
Train
After cleaning and collecting the data, you should write a script to train the Qwen2.5-3B-Instruct model using this created dataset.
The training is using LLaMA-Factory. You should read the dir and write a script to train the model.