🌌 LLM Alignment Template - Your Template for Aligning Language Models

📌 Introduction

LLM Alignment Project

Figure 1: Take a look at: arXiv:2308.05374

LLM Alignment Template is not just a comprehensive tool for aligning large language models (LLMs), but also serves as a powerful template for building your own LLM alignment application. Inspired by project templates like PyTorch Project Template, this repository is designed to provide a full stack of functionality, acting as a starting point to customize and extend for your own LLM alignment needs. Whether you are a researcher, developer, or data scientist, this template provides a solid foundation for efficiently creating and deploying LLMs tailored to align with human values and objectives.

🚀 Overview

LLM Alignment Template provides a full stack of functionality, including training, fine-tuning, deploying, and monitoring LLMs using Reinforcement Learning from Human Feedback (RLHF). This project also integrates evaluation metrics to ensure ethical and effective use of language models. The interface offers a user-friendly experience for managing alignment, visualizing training metrics, and deploying at scale.

✨ Features

• 🌐 Interactive Web Interface: A user-friendly interface for interacting with the LLM, training models, and viewing alignment metrics.
• 🧠 Training with RLHF: Reinforcement Learning from Human Feedback to ensure model alignment with human preferences.
• 🛠️ Data Augmentation & Preprocessing: Advanced preprocessing, tokenization, and data augmentation with back-translation and paraphrasing.
• 🔄 Transfer Learning: Utilize pre-trained models like BERT for improved performance on specific tasks.
• 📦 Scalable Deployment: Docker and Kubernetes-based deployment with Horizontal Pod Autoscaling (HPA).
• 🔍 Model Explainability: SHAP-based dashboards for understanding model decisions.
• 📊 User Feedback Loop: Collection of user ratings for fine-tuning models continuously.

📂 Table of Contents

• Introduction
• Overview
• Features
• Project Structure
• Setup
  • Prerequisites
  • Installation
  • Running Locally
• Deployment
  • Kubernetes Deployment
  • Canary Deployment
  • Monitoring and Logging
• Training and Evaluation
• Testing
• Future Work
• Contributing
• License
• Contact

📂 Project Structure

• app/: Contains API and UI code.

  • auth.py, feedback.py, ui.py: API endpoints for user interaction, feedback collection, and general interface management.
  • Static Files: JavaScript (app.js, chart.js), CSS (styles.css), and Swagger API documentation (swagger.json).
  • Templates: HTML templates (chat.html, feedback.html, index.html) for UI rendering.

• src/: Core logic and utilities for preprocessing and training.

  • Preprocessing (preprocessing/):
  • preprocess_data.py: Combines original and augmented datasets and applies text cleaning.
  • tokenization.py: Handles tokenization.
  • Training (training/):
  • fine_tuning.py, transfer_learning.py, retrain_model.py: Scripts for training and retraining models.
  • rlhf.py, reward_model.py: Scripts for reward model training using RLHF.
  • Utilities (utils/): Common utilities (config.py, logging.py, validation.py).

• dashboards/: Performance and explainability dashboards for monitoring and model insights.

  • performance_dashboard.py: Displays training metrics, validation loss, and accuracy.
  • explainability_dashboard.py: Visualizes SHAP values to provide insight into model decisions.

• tests/: Unit, integration, and end-to-end tests.

  • test_api.py, test_preprocessing.py, test_training.py: Various unit and integration tests.
  • End-to-End Tests (e2e/): Cypress-based UI tests (ui_tests.spec.js).
  • Load Testing (load_testing/): Uses Locust (locustfile.py) for load testing.

• deployment/: Configuration files for deployment and monitoring.

  • Kubernetes Configurations (kubernetes/): Deployment and Ingress configurations for scaling and canary releases.
  • Monitoring (monitoring/): Prometheus (prometheus.yml) and Grafana (grafana_dashboard.json) for performance and system health monitoring.

⚙️ Setup

Prerequisites

• 🐍 Python 3.8+
• 🐳 Docker & Docker Compose
• ☸️ Kubernetes (Minikube or a cloud provider)
• 🟢 Node.js (for front-end dependencies)

📦 Installation

1. Clone the Repository: bash git clone https://github.com/yourusername/LLM-Alignment-Template.git cd LLM-Alignment-Template

2. Install Dependencies:

   • Python dependencies: bash pip install -r requirements.txt
   • Node.js dependencies (optional for UI improvements): bash cd app/static npm install

🏃 Running Locally

1. Build Docker Images: bash docker-compose up --build

2. Access the Application:

   • Open a browser and visit http://localhost:5000.

🚢 Deployment

☸️ Kubernetes Deployment

• Deploy to Kubernetes:
  • Apply the deployment and service configurations: bash kubectl apply -f deployment/kubernetes/deployment.yml kubectl apply -f deployment/kubernetes/service.yml
  • Horizontal Pod Autoscaler: bash kubectl apply -f deployment/kubernetes/hpa.yml

🌟 Canary Deployment

• Canary deployments are configured using deployment/kubernetes/canary_deployment.yml to roll out new versions safely.

📈 Monitoring and Logging

• Prometheus and Grafana:
  • Apply Prometheus and Grafana configurations in deployment/monitoring/ to enable monitoring dashboards.
• 📋 Centralized Logging: The ELK Stack is configured with Docker using docker-compose.logging.yml for centralized logs.

🧠 Training and Evaluation

🔄 Transfer Learning

The training module (src/training/transfer_learning.py) uses pre-trained models like BERT to adapt to custom tasks, providing a significant performance boost.

📊 Data Augmentation

The data_augmentation.py script (src/data/) applies augmentation techniques like back-translation and paraphrasing to improve data quality.

🧠 Reinforcement Learning from Human Feedback (RLHF)

• Reward Model Training: Uses the rlhf.py and reward_model.py scripts to fine-tune models based on human feedback.
• Feedback Collection: Users rate responses via the feedback form (feedback.html), and the model retrains with retrain_model.py.

🔍 Explainability Dashboard

The explainability_dashboard.py script uses SHAP values to help users understand why a model made specific predictions.

🧪 Testing

• ✅ Unit Tests: Located in tests/, covering API, preprocessing, and training functionalities.
• 🖥️ End-to-End Tests: Uses Cypress to test UI interactions.
• 📊 Load Testing: Implemented with Locust (tests/load_testing/locustfile.py) to ensure stability under load.

🔮 Future Work

• 🔑 User Roles and Permissions: Adding a role-based access control system.
• 📉 Advanced Monitoring: Further enhance Prometheus alerts for anomaly detection.
• 🚀 Public Demo Deployment: Deploy a public version on Heroku or AWS for showcasing.

🤝 Contributing

Contributions are welcome! Please submit pull requests or issues for improvements or new features.

📜 License

This project is licensed under the MIT License. See the LICENSE file for more information.

📬 Contact

• 📧 Email: amirsina.torfi@gmail.com
• 🌐 Website: Author Website

Main Collaborators

|
_{Amirsina Torfi} |
_{Hossein Rajoli} | | --- | --- |