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‎README.md

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# LLM Alignment Assistant
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# 🌌 LLM Alignment Assistant - Your Template for Aligning Language Models
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## 🌟 Overview
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## 📌 Introduction
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**LLM Alignment Assistant** is an advanced tool designed to assist in aligning large language models (LLMs) with desired human values and objectives. This project offers a full-stack approach to training, fine-tuning, deploying, and monitoring LLMs using **Reinforcement Learning from Human Feedback (RLHF)**. The system also incorporates evaluation metrics to ensure ethical and effective use of language models. The assistant provides a user-friendly interface for exploring the alignment, visualization of training metrics, and deploying the system at scale using cloud-native technologies.
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**LLM Alignment Assistant** 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. 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.
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![✨ Architecture Diagram](assets/architecture_diagram.png)
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## ✨ Key Features
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## ✨ Features
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- **🖥️ User-Friendly Web Interface**: A sleek, intuitive UI for interacting with the LLM and viewing alignment results.
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- **📊 Interactive Training**: Train models using RLHF, with dynamic metrics displayed in real-time.
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- **🛠️ Data Augmentation & Preprocessing**: Advanced preprocessing scripts, including tokenization, cleaning, and data augmentation using NLP techniques.
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- **⚙️ Scalable Deployment**: Easy deployment via Docker and Kubernetes, with horizontal scaling capabilities.
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- **🔍 Explainability & Monitoring**: Incorporates SHAP or LIME-based explainability features along with live monitoring dashboards.
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- **🌐 Interactive Web Interface**: A user-friendly interface for interacting with the LLM, training models, and viewing alignment metrics.
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- **🧠 Training with RLHF**: Reinforcement Learning from Human Feedback to ensure model alignment with human preferences.
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- **🛠️ Data Augmentation & Preprocessing**: Advanced preprocessing, tokenization, and data augmentation with back-translation and paraphrasing.
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- **🔄 Transfer Learning**: Utilize pre-trained models like BERT for improved performance on specific tasks.
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- **📦 Scalable Deployment**: Docker and Kubernetes-based deployment with Horizontal Pod Autoscaling (HPA).
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- **🔍 Model Explainability**: SHAP-based dashboards for understanding model decisions.
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- **📊 User Feedback Loop**: Collection of user ratings for fine-tuning models continuously.
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## 🗂️ Project Structure
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## 📂 Project Structure
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- **📁 app/**: Contains the UI and the backend logic of the web interface.
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- `ui.py`: Manages routes and interactions with the UI.
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- `static/`: Contains styles and JavaScript for an appealing UI.
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- `templates/`: HTML templates for rendering the web pages.
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- **📁 data/**: Scripts and datasets for generating, downloading, and processing data.
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- **📁 deployment/**: Docker, Kubernetes configurations, and Helm charts to manage deployments.
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- **📁 src/**: Core functionality, including training, evaluation, and preprocessing scripts.
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- **📁 tests/**: Unit and integration tests to ensure quality across the different components.
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- **`app/`**: Contains API and UI code.
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- `auth.py`, `feedback.py`, `ui.py`: API endpoints for user interaction, feedback collection, and general interface management.
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- **Static Files**: JavaScript (`app.js`, `chart.js`), CSS (`styles.css`), and Swagger API documentation (`swagger.json`).
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- **Templates**: HTML templates (`chat.html`, `feedback.html`, `index.html`) for UI rendering.
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## 🛠️ Setup
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- **`src/`**: Core logic and utilities for preprocessing and training.
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- **Preprocessing** (`preprocessing/`):
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- `preprocess_data.py`: Combines original and augmented datasets and applies text cleaning.
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- `tokenization.py`: Handles tokenization.
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- **Training** (`training/`):
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- `fine_tuning.py`, `transfer_learning.py`, `retrain_model.py`: Scripts for training and retraining models.
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- `rlhf.py`, `reward_model.py`: Scripts for reward model training using RLHF.
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- **Utilities** (`utils/`): Common utilities (`config.py`, `logging.py`, `validation.py`).
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### 📋 Prerequisites
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- **`dashboards/`**: Performance and explainability dashboards for monitoring and model insights.
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- `performance_dashboard.py`: Displays training metrics, validation loss, and accuracy.
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- `explainability_dashboard.py`: Visualizes SHAP values to provide insight into model decisions.
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- Python 3.8+
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- Docker & Docker Compose
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- Kubernetes (Minikube or any cloud provider)
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- Node.js (for front-end enhancements)
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- **`tests/`**: Unit, integration, and end-to-end tests.
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- `test_api.py`, `test_preprocessing.py`, `test_training.py`: Various unit and integration tests.
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- **End-to-End Tests** (`e2e/`): Cypress-based UI tests (`ui_tests.spec.js`).
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- **Load Testing** (`load_testing/`): Uses Locust (`locustfile.py`) for load testing.
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### 🔧 Installation
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- **`deployment/`**: Configuration files for deployment and monitoring.
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- **Kubernetes Configurations** (`kubernetes/`): Deployment and Ingress configurations for scaling and canary releases.
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- **Monitoring** (`monitoring/`): Prometheus (`prometheus.yml`) and Grafana (`grafana_dashboard.json`) for performance and system health monitoring.
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1. **📥 Clone the Repository**:
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```bash
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git clone https://github.com/yourusername/LLM-Alignment-Assistant.git
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cd LLM-Alignment-Assistant
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```
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## ⚙️ Setup
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2. **📦 Set Up the Virtual Environment**:
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```bash
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python3 -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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```
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### Prerequisites
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- 🐍 Python 3.8+
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- 🐳 Docker & Docker Compose
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- ☸️ Kubernetes (Minikube or a cloud provider)
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- 🟢 Node.js (for front-end dependencies)
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3. **📦 Install Node.js Dependencies** (optional for enhanced UI):
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### 📦 Installation
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1. **Clone the Repository**:
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```bash
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cd app/static
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npm install
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git clone https://github.com/yourusername/LLM-Alignment-Assistant.git
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cd LLM-Alignment-Assistant
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```
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### 🚀 Running Locally
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2. **Install Dependencies**:
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- Python dependencies:
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```bash
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pip install -r requirements.txt
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```
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- Node.js dependencies (optional for UI improvements):
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```bash
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cd app/static
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npm install
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```
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To run the application locally:
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### 🏃 Running Locally
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1. **🐳 Build Docker Image**:
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1. **Build Docker Images**:
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```bash
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docker-compose up --build
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```
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2. **🌐 Access the UI**:
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Visit `http://localhost:5000` in your web browser.
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2. **Access the Application**:
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- Open a browser and visit `http://localhost:5000`.
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## 📦 Deployment
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## 🚢 Deployment
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### 🚢 Docker and Kubernetes
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### ☸️ Kubernetes Deployment
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- **🐳 Docker**: A Dockerfile is provided for containerization.
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- **☸️ Kubernetes**: Use the provided `deployment/kubernetes/deployment.yml` and `service.yml` files to deploy the app to a Kubernetes cluster.
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- **📜 Helm Charts**: Helm charts are available in the `deployment/helm/` directory for easier reusability and scalability.
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- **Deploy to Kubernetes**:
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- Apply the deployment and service configurations:
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```bash
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kubectl apply -f deployment/kubernetes/deployment.yml
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kubectl apply -f deployment/kubernetes/service.yml
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```
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- **Horizontal Pod Autoscaler**:
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```bash
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kubectl apply -f deployment/kubernetes/hpa.yml
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```
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### 🔄 CI/CD Pipeline
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### 🌟 Canary Deployment
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A GitHub Actions workflow is included to automate building, testing, and deployment:
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- Canary deployments are configured using `deployment/kubernetes/canary_deployment.yml` to roll out new versions safely.
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- **✅ Lint & Test**: Linting and unit tests are run at every pull request.
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- **🐋 Docker Build & Push**: Docker images are built and pushed to Docker Hub.
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- **☸️ Kubernetes Deployment**: Automatically deploy to the Kubernetes cluster upon merging.
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### 📈 Monitoring and Logging
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## 🤖 Training and Fine-Tuning
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- **Prometheus and Grafana**:
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- Apply Prometheus and Grafana configurations in `deployment/monitoring/` to enable monitoring dashboards.
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- **📋 Centralized Logging**: The **ELK Stack** is configured with Docker using `docker-compose.logging.yml` for centralized logs.
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### 💡 Reinforcement Learning from Human Feedback (RLHF)
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## 🧠 Training and Evaluation
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The training module includes:
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- **📊 Fine-Tuning**: Using the `training/fine_tuning.py` script, models can be fine-tuned on specific datasets.
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- **🏆 Reward Models**: Implemented in `training/reward_model.py` for evaluating the appropriateness of responses.
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- **🌐 Distributed Training**: Support for distributed training using `training/rlhf.py`.
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### 🔄 Transfer Learning
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### 🎛️ Hyperparameter Tuning
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The training module (`src/training/transfer_learning.py`) uses pre-trained models like **BERT** to adapt to custom tasks, providing a significant performance boost.
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For hyperparameter tuning, **Optuna** has been integrated to provide automated exploration of the training parameters, ensuring optimal model performance.
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### 📊 Data Augmentation
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## 🔄 Data Pipeline
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The `data_augmentation.py` script (`src/data/`) applies augmentation techniques like back-translation and paraphrasing to improve data quality.
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- **🛠️ Data Augmentation**: Using advanced NLP techniques, including back-translation and embedding-based masking, available in `preprocessing/augmentation.py`.
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- **✅ Validation**: Thorough validation scripts (`preprocess_data.py` and `validate_data.py`) to maintain data quality.
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- **⚙️ Automation with Apache Airflow**: Data pipeline orchestration using Airflow, ensuring proper data flow between stages.
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### 🧠 Reinforcement Learning from Human Feedback (RLHF)
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## 📈 Evaluation and Monitoring
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- **Reward Model Training**: Uses the `rlhf.py` and `reward_model.py` scripts to fine-tune models based on human feedback.
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- **Feedback Collection**: Users rate responses via the feedback form (`feedback.html`), and the model retrains with `retrain_model.py`.
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- **📊 Metrics**: The `evaluation/metrics.py` script provides a detailed analysis of model performance, including bias detection and fairness metrics.
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- **🛡️ Safety Testing**: Ethical AI assessments using `evaluation/safety_tests.py`.
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- **📊 Dashboard**: Real-time monitoring with **Streamlit**, displaying key metrics, including training loss, accuracy, and reward trends.
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### 🔍 Explainability Dashboard
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## 🌐 Web Interface Improvements
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- **🎨 Improved UI with TailwindCSS**: We've enhanced the CSS for modern and engaging aesthetics.
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- **📈 Interactive Visualizations**: Added **Chart.js** visualizations to present alignment metrics in a clear, graphical format.
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- **💬 Chatbot Integration**: A conversational UI element to interact directly with the trained LLM.
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The `explainability_dashboard.py` script uses **SHAP** values to help users understand why a model made specific predictions.
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## 🧪 Testing
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- **✅ Unit Tests**: Located in `tests/`, covering training, preprocessing, and evaluation.
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- **🔄 Integration Tests**: End-to-end tests that simulate full pipeline execution.
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- **🧪 Mock Testing**: Use of `pytest-mock` to simulate API calls and external integrations.
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## 📊 Monitoring and Logging
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- **📈 Monitoring**: Kubernetes monitoring using **Prometheus** and **Grafana**, with Horizontal Pod Autoscaling (HPA) for scalability.
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- **🔍 Explainability**: SHAP and LIME explainability metrics are added to the evaluation process, providing insights into model behavior.
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- **📜 Logging**: Centralized logging using **ELK Stack** (Elasticsearch, Logstash, Kibana).
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## 🚀 Cloud Deployment Instructions (AWS)
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To deploy the LLM Alignment Assistant on **AWS**, you can utilize **Elastic Kubernetes Service (EKS)** or **AWS Sagemaker** for model training:
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1. **AWS Elastic Kubernetes Service (EKS)**:
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- Create an EKS cluster using AWS CLI or the console.
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- Apply the Kubernetes deployment files:
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```bash
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kubectl apply -f deployment/kubernetes/deployment.yml
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kubectl apply -f deployment/kubernetes/service.yml
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```
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- Configure the **Horizontal Pod Autoscaler (HPA)** to ensure scalability:
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```bash
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kubectl apply -f deployment/kubernetes/hpa.yml
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```
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2. **AWS Sagemaker for Model Training**:
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- Modify the `training/fine_tuning.py` to integrate with AWS Sagemaker.
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- Use the Sagemaker Python SDK to launch a training job:
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```python
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import sagemaker
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from sagemaker.pytorch import PyTorch
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role = "arn:aws:iam::your-account-id:role/service-role/AmazonSageMaker-ExecutionRole-2023"
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pytorch_estimator = PyTorch(
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entry_point='training/fine_tuning.py',
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role=role,
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instance_count=1,
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instance_type='ml.p2.xlarge',
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framework_version='1.8.0',
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py_version='py3'
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)
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pytorch_estimator.fit({'training': 's3://your-bucket-name/training-data'})
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```
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- Ensure IAM roles and permissions are properly set for accessing **S3** and **Sagemaker**.
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- **✅ Unit Tests**: Located in `tests/`, covering API, preprocessing, and training functionalities.
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- **🖥️ End-to-End Tests**: Uses **Cypress** to test UI interactions.
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- **📊 Load Testing**: Implemented with **Locust** (`tests/load_testing/locustfile.py`) to ensure stability under load.
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## 🚀 Future Work
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## 🔮 Future Work
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- **🌍 Multi-Language Support**: Expand the LLM's training to support multiple languages.
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- **⚖️ Ethical AI Enhancements**: Further enhance bias detection and mitigation techniques.
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- **☁️ Cloud-Native Deployment**: Implement cloud services like **AWS SageMaker** for training at scale.
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- **🔑 User Roles and Permissions**: Adding a role-based access control system.
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- **📉 Advanced Monitoring**: Further enhance Prometheus alerts for anomaly detection.
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- **🚀 Public Demo Deployment**: Deploy a public version on Heroku or AWS for showcasing.
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## 🤝 Getting Involved
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## 🤝 Contributing
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Contributions are welcome! Feel free to submit issues, pull requests, or suggestions for new features.
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Contributions are welcome! Please submit pull requests or issues for improvements or new features.
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## 📜 License
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
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This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.
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## 📬 Contact
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- **✉️ Email**: [[email protected]](mailto:[email protected])
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- **🌐 Website**: [Portfolio](https://astorfi.github.io)
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- **📧 Email**: [[email protected]](mailto:[email protected])
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- **🌐 Website**: [Your Portfolio](https://astorfi.github.io)
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---
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<p align="center">Made with ❤️ by Amirsina Torfi</p>
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<p align="center">Developed with ❤️ by Your Name</p>

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