This repository offers a curated collection of Python code implementations for common "hand-written" coding problems encountered in Large Language Model (LLM) interviews. It targets engineers and researchers preparing for technical roles in AI and LLMs, providing practical code examples to solidify understanding of fundamental LLM components and algorithms.

How It Works

The project provides direct Python implementations for key LLM building blocks, presented as individual files or notebooks. It covers essential areas such as attention mechanisms (Multi-Head Attention, Grouped Query Attention), positional encodings (Rotary Positional Embeddings), normalization layers (RMSNorm), tokenization (Byte Pair Encoding), and parameter-efficient fine-tuning techniques (LoRA). This approach allows users to study, modify, and run code for specific components, fostering a deeper grasp of their internal logic and application.

Quick Start & Requirements

The provided README does not detail specific installation commands, dependencies, or quick-start guides. Users will likely need a standard Python environment. Depending on the implementation within the notebooks, additional libraries such as PyTorch or TensorFlow may be required. No specific hardware (e.g., GPU, CUDA) or dataset requirements are mentioned.

Highlighted Details

• Features implementations for core LLM components including Multi-Head Attention (MHA), Grouped Query Attention (GQA), Rotary Positional Embeddings (RoPE), Byte Pair Encoding (BPE), and LoRA.
• Includes code for various normalization layers (LayerNorm, RMSNorm) and activation functions (SwiGLU).
• Covers essential loss functions and training paradigms like Cross-Entropy (CE), InfoNCE, Supervised Fine-Tuning (SFT), and Reinforcement Learning methods such as Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO).

Maintenance & Community

No information is available regarding project maintainers, community channels (e.g., Discord, Slack), roadmaps, or sponsorships. The project appears to be a personal collection by the author.

Licensing & Compatibility

The provided README snippet does not specify a software license. Consequently, compatibility for commercial use or closed-source linking cannot be determined.

Limitations & Caveats

The collection is based on the author's personal interview experiences and may not encompass all possible LLM interview topics. The README lacks explicit setup instructions, dependency lists, or usage examples, requiring users to infer these details from the file structure and notebook content. No benchmarks or performance claims are presented.