awesome-llm and Awesome-LLM-Resources-List
These are complements that serve different curation purposes—one focuses on applied AI engineering resources broadly while the other specifically aggregates LLM research papers, training frameworks, and model checkpoints—making them useful to consult together depending on whether you need implementation guidance or foundational research references.
About awesome-llm
XiaomingX/awesome-llm
Awesome-LLM: a curated list of Large Language Model.🔥 大型语言模型（LLM）已经席卷了 全球，不再局限于 NLP 或 AI 社区。这里整理了一些关于大型语言模型，特别是与 ChatGPT 相关的研究论文，涵盖了 LLM 训练框架、部署工具、课程与教程，以及所有公开的 LLM 检查点和 API。
This is a curated list for anyone trying to understand and work with Large Language Models (LLMs). It provides a comprehensive overview of cutting-edge research, open-source models like Llama and DeepSeek, and tools for training, deployment, and evaluation. Researchers, AI engineers, and tech strategists can use this to quickly find relevant papers, frameworks, and trends in the rapidly evolving LLM space, from model checkpoints to training tutorials.
About Awesome-LLM-Resources-List
ilsilfverskiold/Awesome-LLM-Resources-List
A Curated Collection of resources for applied AI engineering (work in progress).
This collection helps AI engineers and practitioners navigate the rapidly evolving landscape of Large Language Model (LLM) tools and platforms. It provides curated lists for hosting private or open-source LLMs, accessing off-the-shelf models via API, and performing local inference. The output is a clear overview of options, features, and pricing to help you make informed decisions for your projects.
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