{"data":{"slug":"mooler0410-llmspracticalguide","name":"LLMsPracticalGuide","tagline":"A curated list of practical guide resources of LLMs","github_url":"https://github.com/Mooler0410/LLMsPracticalGuide","owner":"Mooler0410","repo":"LLMsPracticalGuide","owner_avatar_url":"https://avatars.githubusercontent.com/u/37475129?v=4","primary_language":null,"stars":10196,"forks":788,"topics":["large-language-models","natural-language-processing","nlp","survey"],"archived":false,"github_pushed_at":"2026-04-08T18:26:44+00:00","maintenance_label":"Slowing","url":"https://www.graphcanon.com/tools/mooler0410-llmspracticalguide","markdown_url":"https://www.graphcanon.com/tools/mooler0410-llmspracticalguide.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/mooler0410-llmspracticalguide","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=mooler0410-llmspracticalguide","description":"A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)","homepage_url":"https://arxiv.org/abs/2304.13712v2","license":null,"open_issues":17,"watchers":186,"ai_summary":"This repository provides a collection of practical guides and resources focused on Large Language Models (LLMs), including an evolutionary tree visualizing the development of modern LLMs, along with usage restrictions based on model licensing. The content is continually updated and includes contributions from researchers and practitioners.","readme_excerpt":"

The Practical Guides for Large Language Models

\n\n\n

\n\t\"Awesome\"\t \n

\n\nA curated (still actively updated) list of practical guide resources of LLMs. It's based on our survey paper: [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://arxiv.org/abs/2304.13712) and efforts from @[xinyadu](https://github.com/xinyadu). The survey is partially based on the second half of this [Blog](https://jingfengyang.github.io/gpt). We also build an evolutionary tree of modern Large Language Models (LLMs) to trace the development of language models in recent years and highlights some of the most well-known models. \n\nThese sources aim to help practitioners navigate the vast landscape of large language models (LLMs) and their applications in natural language processing (NLP) applications. We also include their usage restrictions based on the model and data licensing information.\nIf you find any resources in our repository helpful, please feel free to use them (don't forget to cite our paper! 😃). We welcome pull requests to refine this figure! \n\n

\n\n

\n\n\n```bibtex\n @article{yang2023harnessing,\n title={Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond}, \n author={Jingfeng Yang and Hongye Jin and Ruixiang Tang and Xiaotian Han and Qizhang Feng and Haoming Jiang and Bing Yin and Xia Hu},\n year={2023},\n eprint={2304.13712},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n }\n```\n\n## Latest News💥\n- We added usage and restrictions section.\n- We used PowerPoint to plot the figure and released the source file [pptx](./source/figure_gif.pptx) for our GIF figure. [4/27/2023]\n- We released the source file for the still version [pptx](./source/figure_still.pptx), and replaced the figure in this repo with the still version. [4/29/2023]\n- Add AlexaTM, UniLM, UniLMv2 to the figure, and correct the logo for Tk. [4/29/2023]\n- Add usage and Restrictions (for commercial and research purposes) section. Credits to [Dr. Du](https://github.com/xinyadu). [5/8/2023]\n\n\n\n\n## Other Practical Guides for LLMs\n\n- **Why did all of the public reproduction of GPT-3 fail? In which tasks should we use GPT-3.5/ChatGPT?** 2023, [Blog](https://jingfengyang.github.io/gpt) \n- **Building LLM applications for production**, 2023, [Blog](https://huyenchip.com/2023/04/11/llm-engineering.html)\n- **Data-centric Artificial Intelligence**, 2023, [Repo](https://github.com/daochenzha/data-centric-AI)/[Blog](https://towardsdatascience.com/what-are-the-data-centric-ai-concepts-behind-gpt-models-a590071bb727)/[Paper](https://arxiv.org/abs/2303.10158)\n\n\n## Catalog\n* [The Practical Guides for Large Language Models ](#the-practical-guides-for-large-language-models-)\n * [Practical Guide for Models](#practical-guide-for-models)\n * [BERT-style Language Models: Encoder-Decoder or Encoder-only](#bert-style-language-models-encoder-decoder-or-encoder-only)\n * [GPT-style Language Models: Decoder-only](#gpt-style-language-models-decoder-only)\n * [Practical Guide for Data](#practical-guide-for-data)\n * [Pretraining data](#pretraining-data)\n * [Finetuning data](#finetuning-data)\n * [Test data/user data](#test-datauser-data)\n * [Practical Guide for NLP Tasks](#practical-guide-for-nlp-tasks)\n * [Traditional NLU tasks](#traditional-nlu-tasks)\n * [Generation tasks](#generation-tasks)\n * [Knowledge-intensive tasks](#knowledge-intensive-tasks)\n * [Abilities with Scaling](#abilities-with-scaling)\n * [Specific tasks](#specific-tasks)\n * [Real-World ''Tasks''](#real-world-tasks)\n * [Efficiency](#efficiency)\n * [","github_created_at":"2023-04-23T04:22:20+00:00","created_at":"2026-07-07T17:34:04.042054+00:00","updated_at":"2026-08-06T12:01:12.464763+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"}],"tags":[{"slug":"large-language-models","name":"large language models"},{"slug":"natural-language-processing","name":"natural-language-processing"},{"slug":"nlp","name":"nlp"},{"slug":"survey","name":"survey"}],"trust":{"provenance":{"is_fork":false,"github_id":631454728,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-06T12:01:11.691Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":119,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T10:30:47.270Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-06T12:01:12.158Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["- If you are looking for a resource to navigate the landscape of LLMs, this repository provides a comprehensive list of practical guides and resources.","- Use it if you require detailed information about usage restrictions based on model licensing, which can help in legal and compliance considerations.","- Utilize this tool when you need an evolutionary tree that visualizes the development of modern LLMs, aiding in understanding the lineage and progression of different models."],"when_not_to_use":["- Avoid using it if your project does not require a detailed overview or is focused on very specific aspects of LLM implementation where deep, specialized guides are preferred.","- If you prefer hands-on tutorials over curated lists and practical guides, another tool in the same category that offers more interactive content might be better suited.","- This repository may lack real-time updates or ongoing active contributions if other resources provide daily curated inputs from a broader community of contributors."],"source":"enrich:decision_facts","observed_at":"2026-07-11T11:08:13.989Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"LLMsPracticalGuide is a curated list of practical guide resources focused on Large Language Models (LLMs), including an evolutionary tree and licensing details."}]}}