--- title: "Hands-On-Large-Language-Models vs awesome-generative-ai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-steven2358-awesome-generative-ai" tools: ["handsonllm-hands-on-large-language-models", "steven2358-awesome-generative-ai"] --- # Hands-On-Large-Language-Models vs awesome-generative-ai *GraphCanon updated Sep 20, 2026* ## Verdict Pick Hands-On-Large-Language-Models if hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵; pick awesome-generative-ai if awesome-generative-ai is a curated list of resources for deploying and using generative AI models locally, with a focus on open-source tools and platforms. [Hands-On-Large-Language-Models](https://www.llm-book.com/) reports 29k GitHub stars, 6.7k forks, and 40 open issues, last pushed Apr 24, 2026. [awesome-generative-ai](https://github.com/steven2358/awesome-generative-ai) has 13k stars, 2.1k forks, and 682 open issues, last pushed Sep 16, 2026. Figures are from public GitHub metadata via [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models) and [awesome-generative-ai's repository](https://github.com/steven2358/awesome-generative-ai). | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [awesome-generative-ai](/tools/steven2358-awesome-generative-ai.md) | | --- | --- | --- | | Tagline | Code for the book 'Hands-On Large Language Models' | A curated list of modern Generative Artificial Intelligence projects and services | | Stars | 29,162 | 12,651 | | Forks | 6,662 | 2,126 | | Open issues | 40 | 682 | | Language | Jupyter Notebook | - | | Adopt for | Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵 | awesome-generative-ai is a curated list of resources for deploying and using generative AI models locally, with a focus on open-source tools and platforms. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The repository is licensed under CC0-1.0, which is a public domain dedication, allowing for free use, modification, and distribution without attribution. | | Categories | Developer Tools, LLM Frameworks | Developer Tools, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [awesome-generative-ai](/tools/steven2358-awesome-generative-ai.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 146d | 1d | | Open issues (now) | 40 | 682 | | Stars delta | +910 (30d) | +150 (30d) | | Open issues delta | +2 (30d) | +108 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/steven2358-awesome-generative-ai/trust.md) | ## Decision facts: Hands-On-Large-Language-Models - **Adopt for:** Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵 ## Decision facts: awesome-generative-ai - **Requirements:** The repository does not specify a programming language, but many of the listed tools are open-source and may require familiarity with Python or other languages.; Hardware requirements vary depending on the specific tool or model being deployed, with some tools like Rapid-MLX optimized for Apple Silicon. - **Adopt for:** awesome-generative-ai is a curated list of resources for deploying and using generative AI models locally, with a focus on open-source tools and platforms. - **License detail:** The repository is licensed under CC0-1.0, which is a public domain dedication, allowing for free use, modification, and distribution without attribution. ## Choose when ### Choose Hands-On-Large-Language-Models if… - License: Hands-On-Large-Language-Models is Apache-2.0, awesome-generative-ai is CC0-1.0. - Tags unique to Hands-On-Large-Language-Models: book, llms, oreilly, oreilly-books. - Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'. ### Choose awesome-generative-ai if… - License: awesome-generative-ai is CC0-1.0, Hands-On-Large-Language-Models is Apache-2.0. - Requirements: The repository does not specify a programming language, but many of the listed tools are open-source and may require familiarity with Python or other languages.; Hardware requirements vary depending on the specific tool or model being deployed, with some tools like Rapid-MLX optimized for Apple Silicon.. - Tags unique to awesome-generative-ai: ai, awesome-list, generative-ai, generative-art. - Also covers Inference & Serving. - When you need a comprehensive list of open-source tools for local deployment of large language models and other AI services. ## When NOT to use Hands-On-Large-Language-Models - Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. - Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. - If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit. ## When NOT to use awesome-generative-ai - If you require a single, integrated solution for AI deployment rather than a curated list of various tools and platforms. - When you are specifically seeking proprietary or commercial AI services that are not included in the open-source focus of this repository. - If you are only interested in cloud-based AI services and do not require or prefer local deployment options. ## Common questions ### What is the difference between Hands-On-Large-Language-Models and awesome-generative-ai? Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. awesome-generative-ai: A curated list of modern Generative Artificial Intelligence projects and services. See the comparison table for live GitHub stats and shared categories. ### When should I choose Hands-On-Large-Language-Models over awesome-generative-ai? Choose Hands-On-Large-Language-Models over awesome-generative-ai when License: Hands-On-Large-Language-Models is Apache-2.0, awesome-generative-ai is CC0-1.0; Tags unique to Hands-On-Large-Language-Models: book, llms, oreilly, oreilly-books; Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'. ### When should I choose awesome-generative-ai over Hands-On-Large-Language-Models? Choose awesome-generative-ai over Hands-On-Large-Language-Models when License: awesome-generative-ai is CC0-1.0, Hands-On-Large-Language-Models is Apache-2.0; Requirements: The repository does not specify a programming language, but many of the listed tools are open-source and may require familiarity with Python or other languages.; Hardware requirements vary depending on the specific tool or model being deployed, with some tools like Rapid-MLX optimized for Apple Silicon.; Tags unique to awesome-generative-ai: ai, awesome-list, generative-ai, generative-art; Also covers Inference & Serving; When you need a comprehensive list of open-source tools for local deployment of large language models and other AI services. ### When should I avoid Hands-On-Large-Language-Models? Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit. ### When should I avoid awesome-generative-ai? If you require a single, integrated solution for AI deployment rather than a curated list of various tools and platforms. When you are specifically seeking proprietary or commercial AI services that are not included in the open-source focus of this repository. If you are only interested in cloud-based AI services and do not require or prefer local deployment options. ### Is Hands-On-Large-Language-Models or awesome-generative-ai more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (29,162 vs 12,651). Stars measure visibility, not whether either tool fits your constraints. ### Are Hands-On-Large-Language-Models and awesome-generative-ai open source? Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, awesome-generative-ai: CC0-1.0). ### Where can I find alternatives to Hands-On-Large-Language-Models or awesome-generative-ai? GraphCanon lists graph-backed alternatives at [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) and [awesome-generative-ai alternatives](/tools/steven2358-awesome-generative-ai/alternatives) ([Hands-On-Large-Language-Models markdown twin](/tools/handsonllm-hands-on-large-language-models/alternatives.md), [awesome-generative-ai markdown twin](/tools/steven2358-awesome-generative-ai/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/handsonllm-hands-on-large-language-models-vs-steven2358-awesome-generative-ai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Hands-On-Large-Language-Models or awesome-generative-ai? Hands-On-Large-Language-Models: Slowing. awesome-generative-ai: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for Hands-On-Large-Language-Models and awesome-generative-ai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/trust); [awesome-generative-ai trust report](/tools/steven2358-awesome-generative-ai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models`](/api/graphcanon/graph?tool=handsonllm-hands-on-large-language-models) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_