116 lines
4.1 KiB
Markdown
116 lines
4.1 KiB
Markdown
# EmoLLM fine-tuning data generation tutorial
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## **I. Objectives and Background**
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In order to have a better representation of our large mental models, we must have high quality datasets. To achieve this goal, we decided to use four powerful AI grand models: **Wenxin Yiyan**, **Tongyi Qianwen**, **Feifei Spark**, and **Zhipu GLM** 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.
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## **II. dataset generation method**
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1. **Model selection and data preparation**
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Choose four big language models, namely Wenxin Yiyan, Tongyi Qianwen, IFei Spark and Zhipu GLM, obtain the API to call the corresponding interface, and prepare to generate dialogue data.
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3. **Single-turn and multi-turn dialogue data generation**
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Using these four models, we generated 10,000 single and multi-turn conversation data. In doing so, we ensure the diversity, complexity and validity of our data.
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Because mental activity is often complex, in order to ensure the diversity of data. We selected a total of 16 * 28 `448` scenarios for dataset generation. For specific scenario names, please refer to the configuration of the two parameters`emotions_list and areas_of_life`in config.yml.
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4. **Inclusion of self-perception datasets**
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In order to enhance the cognitive ability of the model, we specially added a part of self-cognitive dataset. These datasets help the model better understand the context and improve the naturalness and coherence of the conversation.
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## **III. Practical steps**
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1. **Initialize**
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* Install the required software and libraries
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```bash
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pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
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```
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* Prepare input data and configuration parameters
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See `config.yml` for annotations
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2. **Model selection and configuration**
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* Select the right model for your needs
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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).
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* Make necessary configurations and adjustments to the model
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Use XTuner for fine-tuning based on our dataset and configuration strategy.
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3. **Data generation**
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* Data generation using Tongyi Qianwen
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```bash
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# Terminal operation
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bash run_qwen.bash
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# Or just use python without bash
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python qwen_gen_data_NoBash.py
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```
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* Data generation using Wenxin Yiyan
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```bash
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# Terminal operation
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python ernie_gen_data.py
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```
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* Data generation using Zhipu GLM
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```bash
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# Terminal operation
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python zhipuai_gen_data.py
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```
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* Data generation using IFlystar Fire
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```bash
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# Terminal operation
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python ./xinghuo/gen_data.py
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```
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4. **Integration of self-cognition datasets**
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* Self-cognition dataset this needs to be manually generated in accordance with the format, the following format can be
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```json
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[
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{
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"conversation": [
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{
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"input": "请介绍一下你自己",
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"output": "我是大佬的emo小助手，可以帮助你解决心理上的问题哦"
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}
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]
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},
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{
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"conversation": [
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{
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"input": "请做一下自我介绍",
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"output": "我是大佬的emo小助手，可以帮助你解决心理上的问题哦"
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}
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]
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}
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]
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```
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5. **dataset integration**
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Before dataset 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.
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6. **Evaluation and optimization**
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* Evaluate the generated dataset using appropriate evaluation metrics
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* Make necessary optimizations and adjustments based on the evaluation results
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7. **Testing and deployment**
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* Evaluate the trained model using an independent test set
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* Make necessary adjustments and optimizations based on test results
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* Deploy the final model into a real application |