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RLHF Alignment Template
Full-stack solution for LLM alignment including RLHF training, reward model development, interactive feedback collection interface, model explainability dashboards, and scalable Kubernetes deployment.
Tech Stack
PythonPyTorchRLHFDockerKubernetesSHAP
Problem
Aligning LLMs with human preferences requires complex infrastructure: reward modeling, feedback collection, training pipelines, and deployment - often built from scratch for each project.
Solution
Created a comprehensive template covering the full RLHF stack: data preprocessing, transfer learning, reinforcement learning implementation, web-based feedback collection, and production deployment with Kubernetes.
Impact
Accelerates LLM alignment projects by providing battle-tested infrastructure for the complete RLHF workflow.
Architecture
Web UI (Feedback Collection) → Reward Model Training → RLHF Fine-tuning → Evaluation → Kubernetes Deployment
Key Features
- •RLHF training pipeline with reward modeling
- •Transfer learning support for BERT, GPT, and other pre-trained models
- •Interactive web interface for human feedback collection
- •SHAP-based explainability dashboards for model transparency
- •Docker + Kubernetes deployment with auto-scaling (HPA)
- •Comprehensive evaluation metrics for alignment quality