llm-engineer-toolkit and Awesome-LLM-Resources-List
These are complements—one is a categorized directory of 120+ NLP/LLM libraries for building systems, while the other is a curated collection of applied AI engineering resources and best practices, meant to be consulted together when developing LLM applications.
About llm-engineer-toolkit
KalyanKS-NLP/llm-engineer-toolkit
A curated list of 120+ LLM libraries category wise.
This resource provides a comprehensive, categorized list of over 120 libraries essential for anyone building or working with Large Language Models (LLMs). It helps you quickly identify tools for tasks like training, fine-tuning, application development, and evaluation. This is ideal for AI/ML engineers, data scientists, and researchers focused on integrating LLMs into their projects.
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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