This repository is a curated collection of high-quality resources for Large Language Models (LLMs), covering the entire lifecycle from algorithms and training to deployment and application development. It serves as a comprehensive "toolbox" for researchers, engineers, and practitioners in the LLM space, aiming to consolidate valuable information and facilitate learning and development.

How It Works

The project functions as a structured index of links to articles, code repositories, and surveys related to LLMs. It categorizes resources across key areas such as LLM algorithms (Transformer, GPT, LLaMA, MoE), training, fine-tuning, alignment, inference optimization (KV Cache, Speculative Sampling, vLLM, TensorRT-LLM), data engineering, compression, evaluation, and application development (RAG, Agents). The organization aims to provide a clear path through the complex LLM ecosystem.

Quick Start & Requirements

This repository is a curated list of resources, not a runnable software project. No installation or specific requirements are needed to access the information.

Highlighted Details

• Extensive coverage of Transformer architecture, including detailed explanations and code implementations.
• In-depth resources on LLM training techniques like mixed-precision and distributed training (e.g., Megatron-LM, DeepSpeed).
• Comprehensive guides on LLM inference optimization, including KV Cache, speculative sampling, and frameworks like vLLM and TensorRT-LLM.
• Links to various LLM evaluation benchmarks and leaderboards (Open LLM Leaderboard, HELM, CLEVA).
• Detailed sections on LLMOps, RAG, and prompt engineering methodologies.

Maintenance & Community

The repository is community-driven, with an open invitation for contributions to expand the collection of LLM resources. It includes links to Hugging Face blogs and other community-driven initiatives.

Licensing & Compatibility

The repository itself, as a collection of links and information, does not have a specific software license. The linked resources retain their original licenses.

Limitations & Caveats

As a curated list, the quality and up-to-dateness of the linked external resources are dependent on their original sources. The repository does not provide direct tools or code for LLM development but rather points to them.