The text presents Ellora, a collection of standardized, production-ready methodologies, referred to as recipes, for enhancing Large Language Models (LLMs) through Low-Rank Adaptation (LoRA). This approach is justified by the fact that LoRA achieves performance comparable to full fine-tuning while drastically reducing computational costs and training up to 10,000x fewer parameters. Ellora’s recipes often utilize self-supervised methods like the Magpie approach for data generation and confirm that combining parameter-efficient techniques with reinforcement learning yields significant speed and memory savings. The six structured recipes address diverse operational needs, including recovering model accuracy after quantization, extending context windows up to 2 million tokens, and teaching secure code generation. Specifically, one recipe demonstrates a 97% vulnerability reduction through automated security analysis and Group Relative Policy Optimization (GRPO). Ultimately, Ellora provides concrete, reproducible templates for practitioners to maximize model capabilities efficiently without requiring new, complex training frameworks.