NashKnight

NashKnight / LLM-Whiteboard

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Hands-on modules for LLMs: concise implementations for whiteboard-style practice.

15
0
100% credibility
Found May 21, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

LLM-Whiteboard is a study guide designed for people preparing for AI and machine learning job interviews. It contains detailed notes and PyTorch code examples covering the essential topics you'll need to understand: how AI models pay attention to different parts of text, the building blocks of modern transformer models, different ways AI models learn from data, and how they generate responses. The material is organized into clear chapters so you can study one topic at a time, with code examples written to be easy to read and understand during interview preparation.

How It Works

1
💼 Preparing for an AI interview

You're getting ready for a job interview at an AI company and need to practice writing deep learning code on the spot.

2
🔍 Finding the study guide

You discover a collection of notes that walks through the key code concepts you'll need to explain during interviews.

3
📚 Browse organized chapters

The guide is neatly organized into sections covering attention mechanisms, transformers, training methods, and text generation techniques.

4
Choose your focus area
🧠
Learn Attention Mechanisms

Understand how AI models decide which words matter most when processing text.

🏗️
Study Transformer Building Blocks

Learn the core components that make modern AI models work, explained with code examples.

🎯
Master Training Methods

Review how AI models learn from data using different training approaches.

5
💻 Read the code explanations

Each section includes clear code examples with explanations, so you understand not just what to write but why.

🎉 Walk into your interview with confidence

You now have a solid understanding of the key concepts and can write out the important code implementations from memory.

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AI-Generated Review

What is LLM-Whiteboard?

LLM-Whiteboard is a study guide for developers preparing for LLM algorithm interviews. It provides clean PyTorch implementations of core transformer components you might need to code on a whiteboard during a technical interview. The material covers attention mechanisms (self-attention, cross-attention, multi-head variants, grouped-query attention, and multi-head latent attention), normalization layers, positional encodings like RoPE, activation functions like SwiGLU, and training approaches including DPO, PPO, and GRPO. It also includes decoding strategies like beam search and temperature sampling. The content is organized as markdown files with accompanying PDF versions, split into modular chapters for focused study.

Why is it gaining traction?

Interview prep resources for LLM roles are in high demand as these positions become more competitive. This project fills a specific niche: whiteboard-style coding practice for transformer fundamentals. The implementations prioritize readability over production optimization, which matches how interviews actually work. Having a curated collection of these algorithms in one place saves candidates from scattered research.

Who should use this?

Algorithm engineers and ML researchers preparing for LLM-related technical interviews will find the most value. It's particularly useful for those who understand transformer theory but want to sharpen their ability to implement core components from scratch under pressure. Not suitable for production code reference or learning LLM fundamentals from scratch.

Verdict

This is a niche study tool with significant utility for a specific audience, but the 1.0% credibility score and 15 stars reflect a very early-stage project with minimal community validation. The content appears well-organized, but there are no tests, no examples directory, and no way to verify the implementations work as described. Use it as a supplement to your interview prep, not as authoritative reference material.

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