This repository compiles interview questions and answers for Large Language Model (LLM) algorithm engineers. It serves as a comprehensive study guide covering foundational LLM concepts, advanced topics like attention mechanisms and fine-tuning, and practical aspects such as distributed training and deployment. The target audience is individuals preparing for LLM-related roles in the AI industry, aiming to provide structured knowledge for effective interview preparation.

How It Works

The repository is organized into numerous sections, each dedicated to a specific area within LLM development. It systematically breaks down complex topics into digestible question-and-answer pairs, covering theoretical underpinnings, algorithmic variations, practical implementation challenges, and emerging trends. This structured approach facilitates targeted learning and retention of key information.

Quick Start & Requirements

This repository is a collection of notes and does not require installation or execution. It is intended for reading and study.

Highlighted Details

• Extensive coverage of core LLM components: attention mechanisms (MHA, GQA, MQA, FlashAttention), normalization layers (LayerNorm, RMSNorm), and activation functions.
• Detailed sections on fine-tuning techniques (SFT, PEFT, LoRA, QLoRA) and training strategies (distributed training, incremental pre-training).
• In-depth exploration of Retrieval-Augmented Generation (RAG), including various strategies, optimization techniques, and common pitfalls.
• Comprehensive overview of LLM inference, acceleration methods (vLLM, FasterTransformer), and deployment considerations.

Maintenance & Community

The repository is maintained by km1994. Community interaction is encouraged via a WeChat group, with contact information provided for joining.

Licensing & Compatibility

The repository's content is provided for educational purposes. No specific license is mentioned, implying a permissive use for personal study.

Limitations & Caveats

The repository is a curated collection of interview questions and answers, reflecting the author's experience and understanding. It may not cover all possible interview topics or the latest advancements in the rapidly evolving LLM field.