? LLM Alignment Template - Your Template for Aligning Language Models

? Introduction

LLM Alignment Project

Figure 1: Overview of the LLM Alignment Project. 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.

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

Clone the Repository:

git clone https://github.com/yourusername/LLM-Alignment-Template.git
cd LLM-Alignment-Template

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

? Running Locally

Build Docker Images:
```
docker-compose up --build
```
Access the Application:
- Open a browser and visit http://localhost:5000.

? Deployment

☸️ Kubernetes Deployment

Deploy to Kubernetes:

Apply the deployment and service configurations:

kubectl apply -f deployment/kubernetes/deployment.yml
kubectl apply -f deployment/kubernetes/service.yml

Horizontal Pod Autoscaler:

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.