AutoRLAIF: Automated Reinforcement Learning from AI Feedback for Large Language Models

AutoRLAIF is a cutting-edge framework designed to revolutionize the fine-tuning of large language models through Reinforcement Learning from AI Feedback (RLAIF). By automating the supervised fine-tuning (SFT) process, AutoRLAIF eliminates the need for extensive manual intervention, enhancing both efficiency and performance in developing sophisticated AI-driven conversational systems.

🚀 Key Features

• Automated Fine-Tuning: Leverages RLAIF to autonomously refine language models based on AI-generated feedback, minimizing the reliance on human supervision.
• High-Efficiency Training: Utilizes advanced techniques such as QLoRA and Parameter-Efficient Fine-Tuning (PEFT) to optimize training speed and resource utilization.
• Data Integration: Combines multiple high-quality datasets, including:
  • lmsys-arena-human-preference-55k: Comprehensive human preference data.
  • lmsys-chatbot_arena_conversations-33k: Extensive chatbot conversation logs.
  • lmsys-Pairs-generated-from-lmsys-1M-dataset: Large-scale AI-generated data pairs.
• Advanced Training Techniques:
  • LoRA (Low-Rank Adaptation): Enhances model adaptability with minimal parameter updates.
  • EMA (Exponential Moving Average): Stabilizes training by maintaining a moving average of model parameters.
  • R-Drop: Improves model robustness through regularization techniques.
• Flexible Inference: Implements Test-Time Augmentation (TTA) to ensure consistent and accurate predictions during the inference phase.
• Scalable Architecture: Designed to handle large-scale datasets and models, making it suitable for extensive AI applications across various domains.

🛠️ Technologies Used

• Deep Learning Frameworks: PyTorch, Transformers
• Fine-Tuning Tools: QLoRA, PEFT
• Data Processing: Datasets, NumPy, Pandas
• Optimization Tools: BitsAndBytes, DeepSpeed, Scikit-learn
• Others: Matplotlib, Seaborn

🎯 Applications

AutoRLAIF is ideal for developers, researchers, and organizations aiming to enhance their language models with minimal manual effort. Key applications include:

• AI-Driven Chatbots: Develop intelligent conversational agents that understand and respond to user preferences accurately.
• User Preference Prediction: Implement systems that can predict and adapt to user preferences in real-time.
• Content Generation: Create high-quality, contextually relevant content across various platforms and industries.
• Research and Development: Facilitate advanced research in natural language processing and machine learning by providing a robust framework for model fine-tuning.

📂 Directory Structure

AutoRLAIF/
├── README.md
├── LICENSE
├── data/
│   ├── lmsys-arena-human-preference-55k/
│   │   └── train.csv
│   ├── lmsys-chatbot_arena_conversations-33k/
│   │   └── train.csv
│   └── lmsys-Pairs-generated-from-lmsys-1M-dataset/
│       └── train.csv
├── src/
│   ├── data_processing/
│   │   └── custom_tokenizer.py
│   ├── model_training/
│   │   ├── train.py
│   │   ├── ema.py
│   │   └── rdrop.py
│   ├── model_evaluation/
│   │   └── metrics.py
│   ├── inference/
│   │   └── inference.py
│   ├── configs/
│   │   └── config.py
│   └── utils/
│       └── callbacks.py
├── doc/
│   └── Kaggle_Large_Model_Competition_Technical_Report.md
├── examples/
│   └── example_usage.ipynb
└── requirements.txt

📦 Installation

1. Clone the Repository

   bash
   git clone https://github.com/your_username/AutoRLAIF.git
   cd AutoRLAIF
   

2. Install Dependencies

   bash
   pip install -r requirements.txt
   

3. Download Models and Datasets

   • Pre-trained Model: Download the pre-trained Gemma-2-9b-it model and place it in the ./pretrained_models/gemma-2-9b-it-4bit directory.
   • Datasets: Download and extract the required datasets into the data/ directory, ensuring the directory structure matches the one outlined above.

🏃 Usage

🔥 Training the Model

Navigate to the src/model_training/ directory and execute the training script:

bash
python train.py

Configuration: Modify training parameters in src/configs/config.py as needed.

🧠 Model Inference

After training, navigate to the src/inference/ directory and run the inference script:

bash
python inference.py

📓 Example Notebook

Refer to examples/example_usage.ipynb for a comprehensive guide on setting up, training, and performing inference with AutoRLAIF.

📄 License

This project is licensed under the Apache License 2.0.

🤝 Contributing

Contributions are welcome! Please read Kaggle大模型竞赛技术报告.md for guidelines on how to proceed.

📧 Contact

For any questions or suggestions, please contact [email protected].