{"data":{"slug":"mooler0410-llmspracticalguide","name":"LLMsPracticalGuide","tagline":"Curated list of practical guide resources for 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":10208,"forks":787,"topics":["large-language-models","natural-language-processing","nlp","survey"],"archived":false,"github_pushed_at":"2026-04-08T18:26:44+00:00","maintenance_label":"Slowing","stars_delta_30d":12,"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":185,"ai_summary":"A curated list of practical guide resources for large language models (LLMs), including an LLMs tree, examples, and papers, aimed at helping practitioners navigate the landscape of LLMs and their applications in NLP.","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":"2026-09-20T04:33:43.061563+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"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":"2026-09-18T08:49:57.322Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":162,"last_release_at":null,"stars_delta_30d":12,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-09-18T08:49:59.556Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-18T08:49:56.275Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need a comprehensive overview of LLMs, including their development history and practical applications in NLP.","If you are looking for a curated list of resources that includes both models and data considerations for LLMs.","When you require detailed information on usage restrictions and licensing for various LLMs, which can be crucial for both commercial and research purposes."],"when_not_to_use":["If you are seeking a tool for direct implementation or deployment of LLMs, as LLMsPracticalGuide is a repository of resources and not a framework or library.","When you need real-time support or updates on the latest LLMs, as the repository, while actively updated, may not cover the most recent models or developments.","If you are looking for a hands-on coding environment or a platform for experimenting with LLMs, as LLMsPracticalGuide is primarily a resource guide and does not provide a coding or experimentation lab"],"source":"enrich:decision_facts","observed_at":"2026-09-18T10:42:43.030Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"LLMsPracticalGuide is a curated list of practical guide resources for large language models (LLMs), including an LLMs tree, examples, and papers, aimed at helping practitioners navigate the landscape of LLMs and their N-"}]}}