LLMsPracticalGuide
A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
A curated reference guide for large language models: a family tree showing how models like GPT-4, BERT, and LLaMA relate historically, plus organized paper lists and guidance on picking the right model for tasks like summarization or code generation.
This repository is a curated collection of resources about large language models (LLMs), built as a companion to a research survey paper. It functions as a reference guide that maps out the major AI language models and how to apply them in practice.
The centerpiece is a family tree diagram showing how different language models relate to each other historically, tracing a lineage from early models like BERT and GPT through to more recent ones like GPT-4 and LLaMA. The tree shows which systems descended from or were influenced by earlier work, giving readers a timeline of how the field developed over several years.
The resource catalog is organized into three main areas. For models, it separates "BERT-style" systems, which are generally better at understanding and classifying text, from "GPT-style" systems, which are generally better at generating new text. For each model, the repository links to the original research paper. For data, it covers guidance on pretraining data, fine-tuning data, and test data. For specific use cases, it lists guidance on tasks like summarization, question answering, translation, and code generation, noting which types of models tend to perform well on which tasks.
The guide also includes a section on usage restrictions, documenting which models allow commercial use and which are limited to research purposes. AI model licensing varies considerably, and this section helps practitioners understand what they can and cannot do with a given model before building on it.
This is a reference resource, not a software tool. It contains no runnable code. Its value is as an organized index of papers and context for choosing an LLM for a specific task. The full README is longer than what was shown.
Where it fits
- Look up which type of language model, BERT-style or GPT-style, works best for a specific task like classification or text generation.
- Check which LLMs allow commercial use before building a product that depends on one.
- Trace the historical lineage of AI language models to understand how the field has evolved over time.
- Find the original research paper for any major language model released in recent years.