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Fine-Tuning Process Using the "Model Builder Interface, SUDOBRAIN Training Toolkit UI"

1. Introduction

The provided application is a GUI based python script designed to facilitate the fine-tuning of OpenAI's GPT-3.5-turbo model. The application allows users to:

• Input their OpenAI API key.
• Select a folder containing .txt files.
• Process these .txt files using the GPT-3.5-turbo model.
• Convert the processed content into a dataset format suitable for fine-tuning.
• Initiate the fine-tuning process using the OpenAI API.
• View conversion logs and edit .txt files.
• Display the folder contents and results of the operations.

2. Key Functions

2.1 process_with_chatgpt(txt_content, api_key)

• Purpose: Processes a given text content using the GPT-3.5-turbo model.
• Parameters:
  • txt_content: The text to be processed.
  • api_key: The OpenAI API key.
• Process:
  • The function sets the OpenAI API key.
  • It then sends the txt_content to the OpenAI API for processing.
  • The processed response is returned.

2.2 content_to_dataset(validated_content, dataset_file)

• Purpose: Converts the processed content into a dataset format.
• Parameters:
  • validated_content: The processed content.
  • dataset_file: The file where the dataset will be saved.
• Process:
  • The function writes the validated_content to the dataset_file in JSON format.

2.3 train_model(api_key, training_file)

• Purpose: Initiates the fine-tuning process using the OpenAI API.
• Parameters:
  • api_key: The OpenAI API key.
  • training_file: The dataset file to be used for fine-tuning.
• Process:
  • The function sets the OpenAI API key.
  • It sends the training_file to the OpenAI API for fine-tuning.
  • The ID of the fine-tuning job is returned.

2.4 convert_files(folder_path, api_key)

• Purpose: Processes .txt files in a given folder and converts them into a dataset format.
• Parameters:
  • folder_path: The path to the folder containing the .txt files.
  • api_key: The OpenAI API key.
• Process:
  • For each .txt file in the folder_path, the content is read and processed using the GPT-3.5-turbo model.
  • The processed content is then added to the dataset.
  • The dataset is saved in the folder_path as "dataset.json".

3. GUI Components

The script uses tkinter to create a graphical user interface. Key components include:

• API Configuration Section: Allows users to input their OpenAI API key.
• File and Training Section: Provides buttons to select a folder with .txt files and start the training process.
• Logs and Editing Section: Allows users to view conversion logs and edit .txt files.
• Folder Contents Section: Displays the contents of the selected folder.
• Results Section: Displays the results of the operations.

4. Conclusion

The "Model Builder Interface, SUDOBRAIN Training Toolkit UI" script provides a comprehensive tool for users to fine-tune the GPT-3.5-turbo model using their own .txt files. The process involves processing the .txt files, converting them into a dataset format, and initiating the fine-tuning process using the OpenAI API.

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