project_plan.md

LLM Finetuner Project Plan

1. Project Overview

The LLM Finetuner is a user-friendly application designed to simplify the process of fine-tuning Large Language Models (LLMs) using the Unsloth library. The application provides a graphical user interface for dataset preparation, model selection, fine-tuning, testing, and GGUF conversion.

2. Project Structure

llm_finetuner/
├── main.py
├── ui.py
├── model_utils.py
├── dataset_utils.py
├── training_utils.py
├── inference_utils.py
├── gguf_utils.py
├── requirements.txt
└── README.md

3. Key Components

3.1 User Interface (ui.py)

• Gradio-based interface with tabs for different functionalities
• Handles user inputs and interactions
• Coordinates between different modules

3.2 Model Utilities (model_utils.py)

• Handles model loading and initialization
• Supports various pre-trained models from Unsloth

3.3 Dataset Utilities (dataset_utils.py)

• Manages dataset preparation from Hugging Face and local files
• Implements synthetic dataset creation using AI providers (OpenAI, Anthropic, Ollama)

3.4 Training Utilities (training_utils.py)

• Implements the fine-tuning process using Unsloth and TRL

3.5 Inference Utilities (inference_utils.py)

• Handles model testing and inference

3.6 GGUF Conversion Utilities (gguf_utils.py)

• Manages the conversion of fine-tuned models to GGUF format

4. Implementation Plan

4.1 Phase 1: Core Functionality

• Implement basic UI structure
• Develop model loading and initialization
• Implement dataset preparation for Hugging Face and local files using the model transformers and chat template.
• Develop basic fine-tuning functionality using the prepared dataset
• Implement model testing
• Add GGUF conversion capability

4.2 Phase 2: Enhanced Features

• Implement synthetic dataset creation
• Improve error handling and user feedback
• Implement progress tracking for long-running operations
• Add support for custom model configurations

4.3 Phase 3: Optimization and Advanced Features

• Optimize performance for large datasets and models
• Implement advanced fine-tuning techniques (e.g., LoRA, QLoRA)
• Add support for distributed training
• Implement model comparison tools

5. Testing Plan

5.1 Unit Testing

• Develop unit tests for each utility module
• Ensure proper error handling and edge case coverage

5.2 Integration Testing

• Test the interaction between different modules
• Verify data flow from UI to backend and vice versa

5.3 User Acceptance Testing

• Conduct usability testing with potential users
• Gather feedback on UI intuitiveness and feature completeness

6. Deployment Plan

6.1 Local Deployment

• Provide clear instructions for local installation and setup
• Create a comprehensive README with usage guidelines

6.2 Cloud Deployment (Future Consideration)

• Explore options for cloud deployment (e.g., Hugging Face Spaces, Google Cloud)
• Implement necessary security measures for cloud deployment

7. Documentation

• Create user documentation explaining each feature and its usage
• Develop technical documentation for future maintainers
• Include examples and use cases in the documentation

8. Maintenance and Updates

• Establish a process for regular updates to supported models and libraries
• Plan for ongoing bug fixes and feature enhancements based on user feedback

This project plan provides a roadmap for the development, testing, and deployment of the LLM Finetuner application. It should be reviewed and updated regularly as the project progresses and new requirements or challenges emerge.