EMO Psychological large model fine-tuning data generation tutorial

I. Objectives and Background

In order to have a better representation of our large mental models, we must have high quality data sets. To achieve this goal, we decided to use four powerful AI grand models: Wenxin Yiyi, Tongyi Qianwen, Feifei Spark, and NXP AI to generate conversation data. In addition, we will enhance the cognitive depth of the dataset and improve the generalization ability of the model by adding a small number of self-cognitive datasets.

II. Data set generation method

Model selection and data preparation

Choose four big language models, namely Wenxin Yiyi, Tongyi Qianwen, IFei Spark and Zhipu, obtain the API to call the corresponding interface, and prepare to generate dialogue data.
**Single round and multiple round dialogue data generation **

Using these four models, we generated 10,000 single - and multi-round conversation data. In doing so, we ensure the diversity, complexity and validity of our data.

Because mental activity is often complex, in order to ensure the diversity of data. We selected a total of 16 * 28 448 scenarios for data set generation. For specific scenario names, please refer to the configuration of the two parametersemotions_list and areas_of_lifein config.yml.
Inclusion of self-perception datasets

In order to enhance the cognitive ability of the model, we specially added a part of self-cognitive data set. These data sets help the model better understand the context and improve the naturalness and coherence of the conversation.

III. Practical steps

Initialize

Install the required software and libraries.

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

Prepare input data and configuration parameters.

See config.yml for annotations

Model selection and configuration

Select the right model for your needs. In order to enable everyone to play with the large model, we chose the InterLLM2-7B as our baseline model (consumer graphics cards can also be deployed fine-tuned oh).
Make necessary configuration and adjustments to the model. Use XTuner for fine-tuning based on our data set and configuration strategy

Data generation

Data generation using Tongyi Qianwen large model.
```
# Terminal operation
bash run_qwen.bash
```
Use Baidu Wenxin large model for data generation.
```
# Terminal operation
python ernie_gen_data.py
```

Data generation using the NXP AI large model.

# Terminal operation
python zhipuai_gen_data.py

Use IFlystar Fire model for data generation.

# Terminal operation
python ./xinghuo/gen_data.py

Integration of self-cognition datasets

Self-cognition data set this needs to be manually generated in accordance with the format, the following format can be.

[
    {
        "conversation": [
            {
                "input": "请介绍一下你自己",
                "output": "我是大佬的emo小助手，可以帮助你解决心理上的问题哦"
            }
        ]
    },
    {
        "conversation": [
            {
                "input": "请做一下自我介绍",
                "output": "我是大佬的emo小助手，可以帮助你解决心理上的问题哦"
            }
        ]
    }
]

Data set integration.

Before data set integration, we need to check whether the generated data has formatting errors, type mismatches, etc. We need check.py to check the data. Finally, merge_json.py is used to combine all the json into one overall json file.
Evaluation and optimization

Evaluate the generated dataset using appropriate evaluation metrics.
Make necessary optimizations and adjustments based on the evaluation results.

Testing and deployment

Evaluate the trained model using an independent test set.
Make necessary adjustments and optimizations based on test results.
Deploy the final model into a real application.

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