This repository is a curated collection of Jupyter notebooks implementing core components of modern LLM architectures, intended for interview preparation. It covers attention mechanisms (MHA, GQA, MLA, KV-cache), normalization layers (LayerNorm, RMSNorm), functional components (RoPE, SwiGLU, LoRA, BPE, activation functions, cross-entropy, InfoNCE, sampling, quantization, SFT loss, tool calling parsing), and reinforcement learning losses (DPO, GRPO, DAPO, GSPO, KL divergence). Each notebook is a self-contained, minimal implementation of a specific algorithm, typically using PyTorch. The code is clean, concise, and well-commented, reflecting good engineering practices for educational purposes. While the repository is not a production application and lacks data pipelines, model training scripts, or continuous integration, it serves as a high-quality reference for understanding and reproducing the mathematical foundations of transformer-based language models. The hybrid nature lies in its focus on research-oriented ML components, but its structure is a pure code library rather than a data-driven research project.