https://github.com/astorfi/llm-alignment-project
A comprehensive template for aligning large language models (LLMs) using Reinforcement Learning from Human Feedback (RLHF), transfer learning, and more. Build your own customizable LLM alignment solution with ease.
https://github.com/astorfi/llm-alignment-project
ai alignment deep-learning generative-ai large-language-models llms machine-learning rlhf template
Last synced: 6 months ago
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A comprehensive template for aligning large language models (LLMs) using Reinforcement Learning from Human Feedback (RLHF), transfer learning, and more. Build your own customizable LLM alignment solution with ease.
- Host: GitHub
- URL: https://github.com/astorfi/llm-alignment-project
- Owner: astorfi
- License: mit
- Created: 2024-11-23T06:16:56.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-15T05:25:50.000Z (about 1 year ago)
- Last Synced: 2025-07-17T17:55:18.694Z (6 months ago)
- Topics: ai, alignment, deep-learning, generative-ai, large-language-models, llms, machine-learning, rlhf, template
- Language: Python
- Homepage:
- Size: 619 KB
- Stars: 33
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: docs/CODE_OF_CONDUCT.md
- Security: docs/SECURITY.md
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README
# 🌌 LLM Alignment Template - Your Template for Aligning Language Models
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[](https://opensource.org/licenses/MIT)
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[](https://www.python.org/)
## 📌 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](#-introduction)
- [Overview](#-overview)
- [Features](#-features)
- [Project Structure](#-project-structure)
- [Setup](#️-setup)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Running Locally](#running-locally)
- [Deployment](#-deployment)
- [Kubernetes Deployment](#kubernetes-deployment)
- [Canary Deployment](#canary-deployment)
- [Monitoring and Logging](#monitoring-and-logging)
- [Training and Evaluation](#-training-and-evaluation)
- [Testing](#-testing)
- [Future Work](#-future-work)
- [Contributing](#-contributing)
- [License](#-license)
- [Contact](#-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](LICENSE) file for more information.
## 📬 Contact
- **📧 Email**: [amirsina.torfi@gmail.com](mailto:amirsina.torfi@gmail.com)
- **🌐 Website**: [Author Website](https://astorfi.github.io)
## Main Collaborators
| [
](https://github.com/astorfi)
[Amirsina Torfi](https://github.com/astorfi) | [
](https://github.com/HRajoliN)
[Hossein Rajoli](https://github.com/HRajoliN) |
| --- | --- |