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# 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
- [x] Implement basic UI structure
- [x] Develop model loading and initialization
- [x] Implement dataset preparation for Hugging Face and local files using the model transformers and chat template.
- [x] Develop basic fine-tuning functionality using the prepared dataset
- [x] Implement model testing
- [x] Add GGUF conversion capability
### 4.2 Phase 2: Enhanced Features
- [x] 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. |