This repository is a comprehensive learning resource for individuals aiming to understand Large Language Models (LLMs) and prepare for LLM research or development interviews. It covers foundational knowledge in deep learning, natural language processing, LLM specifics, and practical aspects like inference and application development.

How It Works

The repository organizes learning materials into distinct sections, starting with fundamental deep learning concepts like Transformer architecture and self-attention mechanisms, including mathematical formulations and PyTorch code examples. It then delves into NLP basics such as tokenization, classic NLP models, and perplexity. Core LLM topics include training frameworks (Megatron-LM, DeepSpeed), parameter-efficient fine-tuning (PEFT) methods like LoRA, and an overview of popular open-source LLMs (Llama, ChatGLM, BLOOM). The resource also covers LLM inference techniques, cost considerations, and applications like LangChain, alongside discussions on cutting-edge research and papers.

Quick Start & Requirements

This repository is a curated collection of learning materials, not a runnable software package. It requires no installation. Users will need to access external resources and potentially run code examples using Python and PyTorch.

Highlighted Details

• Detailed explanations and code for Transformer self-attention, including the rationale behind scaling.
• In-depth comparisons of normalization techniques (BN vs. LN) and optimizers (SGD, Adam, AdamW).
• Comprehensive coverage of tokenization methods (BPE, WordPiece, Unigram) and their implications for different languages.
• Explanations of advanced LLM concepts like Mixture-of-Experts (MoE), RLHF, Chain-of-Thought (CoT), and Tree of Thoughts (ToT).
• Analysis of LLM inference parameters (temperature, top-p, top-k) and the cost difference between input and output tokens.

Maintenance & Community

This is a community-driven "awesome" list, maintained by users contributing resources. There are no specific maintainers or community channels mentioned in the README.

Licensing & Compatibility

The repository itself is not licensed as software. The linked resources and papers will have their own respective licenses.

Limitations & Caveats

As a curated list, the quality and up-to-dateness of external resources are not guaranteed. The repository does not provide runnable code for LLMs themselves, only explanations and examples.