My LLM Learning Journey

This repository documents my learning journey through Large Language Models (LLMs), from basic NLP tasks to advanced fine-tuning techniques.

What I'm Learning

1. NLP with Hugging Face Transformers

File: Exploring NLP through Hugging Face Transformers Library.ipynb

My exploration of various NLP tasks:

• Sentiment Analysis: Learning to use DistilBERT for emotion detection
• Emotion Recognition: Working with RoBERTa for advanced emotion classification
• Text Summarization: Experimenting with automated text summarization
• Language Translation: Testing multi-language translation capabilities
• Text Generation: Trying creative text generation with GPT-2
• Named Entity Recognition: Extracting entities from text
• Question Answering: Building context-based question answering systems
• Fill-Mask: Exploring text completion and mask filling

What I've implemented:

• Interactive visualizations and analysis
• Error handling and edge case testing
• Batch processing capabilities
• Performance metrics and evaluation

2. Empathy Chatbot with GPT-3.5 Fine-tuning

Files:

• Empathy_Chatbot_Improved.ipynb (My enhanced version)
• Prompt_engineering.ipynb (My initial attempt)

My deep dive into fine-tuning:

• Secure API Management: Learning environment-based API key handling
• Data Processing: Cleaning and preparing empathetic dialogues dataset
• Cost Estimation: Understanding fine-tuning costs before training
• Training Monitoring: Implementing real-time progress tracking
• Model Evaluation: Building comprehensive testing frameworks
• Production Ready: Adding error handling, logging, and retry logic

Dataset: Empathetic Dialogues (Facebook AI Research) Model: GPT-3.5-turbo fine-tuning for empathetic responses

How to Run My Projects

1. Clone the Repository

git clone <repository-url>
cd LLMs

2. Install Dependencies

pip install -r requirements.txt

3. Validate Setup

python test_setup.py

4. Environment Setup (Optional)

For my GPT-3.5 fine-tuning project, set up your OpenAI API key:

# Create .env file
echo "OPENAI_API_KEY=your_api_key_here" > .env

Or set environment variable:

export OPENAI_API_KEY="your_api_key_here"

How I Use These Projects

Running My NLP Transformers Notebook

1. Launch Jupyter: jupyter notebook
2. Open Exploring NLP through Hugging Face Transformers Library.ipynb
3. Run cells sequentially to explore different NLP tasks

Running My Empathy Chatbot Project

1. Open Empathy_Chatbot_Improved.ipynb
2. Follow the security guidelines for API key setup
3. Review cost estimates before starting fine-tuning
4. Monitor training progress and evaluate results

Security Best Practices I Follow

• Never commit API keys to version control
• Use environment variables for sensitive data
• Validate inputs and handle errors gracefully
• Monitor costs and usage limits
• Follow OpenAI's usage policies

My Project Structure

LLMs/
├── README.md                                          # This file
├── requirements.txt                                   # Python dependencies
├── test_setup.py                                     # Setup validation script
├── Exploring NLP through Hugging Face Transformers Library.ipynb  # My NLP experiments
├── Empathy_Chatbot_Improved.ipynb                   # My enhanced chatbot project
└── Prompt_engineering.ipynb                         # My initial chatbot attempt

My Learning Progress

Beginner Level - Completed

• Understanding transformer architectures
• Using pre-trained models for various NLP tasks
• Data preprocessing and visualization
• Basic prompt engineering techniques

Intermediate Level - In Progress

• Fine-tuning pre-trained models
• Custom dataset preparation
• Model evaluation and metrics
• Cost optimization for training

Advanced Level - Future Goals

• Production-ready code architecture
• Error handling and monitoring
• Security best practices
• Scalable deployment considerations

What I've Improved Over Time

Code Quality

• Modular Design: Learned object-oriented programming with clear separation of concerns
• Error Handling: Added comprehensive exception handling and logging
• Type Hints: Enhanced code readability and IDE support
• Documentation: Added detailed docstrings and comments

Performance

• Batch Processing: Implemented efficient handling of multiple inputs
• Memory Management: Optimized data loading and processing
• Progress Tracking: Added real-time monitoring with progress bars

Security

• API Key Management: Learned secure handling of sensitive credentials
• Input Validation: Added sanitization and validation of user inputs
• Cost Control: Implemented estimation and monitoring of API usage costs

User Experience

• Interactive Testing: Built real-time model interaction
• Visualization: Created rich charts and graphs for data analysis
• Clear Feedback: Added informative success and error messages

What I Plan to Learn Next

1. Expand Model Coverage: Experiment with more transformer variants (BERT, T5, etc.)
2. Custom Training: Learn training from scratch techniques
3. Deployment: Explore containerization and cloud deployment
4. Evaluation Metrics: Study advanced model evaluation techniques
5. Advanced Techniques: Dive into RAG, agents, and tool calling

Contributing

Feel free to contribute by:

• Adding new NLP tasks and models
• Improving documentation and tutorials
• Reporting bugs and suggesting enhancements
• Sharing your own LLM experiments

License

This project is for educational purposes. Please respect the licenses of the underlying models and datasets used.

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