This repository provides a curated list of 63 interview questions and answers focused on Large Language Models (LLMs), targeting individuals preparing for machine learning and data science roles. It aims to consolidate essential knowledge about LLM concepts, architectures, and applications, serving as a comprehensive study guide.

How It Works

The content is structured around key LLM topics, including the Transformer architecture, attention mechanisms, positional encodings, pre-training/fine-tuning, and various applications like sentiment analysis and conversational AI. Each question is answered with detailed explanations, often accompanied by Python code snippets illustrating core concepts and implementations using libraries like PyTorch and TensorFlow.

Quick Start & Requirements

• Access: All questions and answers are available directly within the README or via the linked Devinterview.io page.
• Prerequisites: Familiarity with Python, machine learning concepts, and deep learning frameworks (PyTorch/TensorFlow) is assumed. No direct code execution or installation is required to benefit from the content.
• Resources: The primary resource is the README file itself, with links to the Devinterview.io website for a consolidated view.

Highlighted Details

• Comprehensive coverage of fundamental LLM concepts.
• Practical Python code examples for core mechanisms (e.g., self-attention, positional encoding).
• Explanations of key architectures like Transformers, BERT, and GPT.
• Discussion of LLM applications across various domains (healthcare, finance, etc.).
• Insights into advanced techniques like few-shot learning and prompt engineering.

Maintenance & Community

The repository appears to be a static collection of information, with no explicit mention of active maintenance, community channels (like Discord/Slack), or a roadmap. It is primarily a knowledge resource.

Licensing & Compatibility

The repository does not explicitly state a license. The content is presented for educational purposes.

Limitations & Caveats

This repository is a static Q&A resource and does not provide executable code or a framework for building LLMs. The information reflects common LLM knowledge up to the point of its creation and may not include the very latest advancements.