--- title: "Hands-On-Large-Language-Models vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/handsonllm-hands-on-large-language-models-vs-rasbt-llms-from-scratch" tools: ["handsonllm-hands-on-large-language-models", "rasbt-llms-from-scratch"] --- # Hands-On-Large-Language-Models vs LLMs-from-scratch Neutral, constraint-first comparison with live GitHub stats. | | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | Official code repo for the O'Reilly Book - 'Hands-On Large Language Models' | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 27,427 | 98,748 | | Forks | 6,389 | 15,153 | | Open issues | 37 | 4 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | The 'Hands-On Large Language Models' repository, backed by Jay Alammar and Maarten Grootendorst, is a comprehensive collection of code examples from their book on large language models. It's designed to simplify the use, | LLMs-from-scratch is a repository that offers detailed, step-by-step guidance on developing, pretraining, and finetuning GPT-like large language models using PyTorch. The codebase complements a book dedicated to building | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | LLM Frameworks, Developer Tools | LLM Frameworks, Model Training | ## 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) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Days since push | 75d | 35d | | Open issues (now) | 37 | 4 | | Owner type | Organization | User | | Security scan | 96 low (96 low) | 34 low (34 low) | | Full report | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | **Typed relationship:** Hands-On-Large-Language-Models _(alternative)_ LLMs-from-scratch Both repositories are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book. ## Decision facts: Hands-On-Large-Language-Models - **Adopt for:** The 'Hands-On Large Language Models' repository, backed by Jay Alammar and Maarten Grootendorst, is a comprehensive collection of code examples from their book on large language models. It's designed to simplify the use, ## Decision facts: LLMs-from-scratch - **Requirements:** Min 8 GB RAM; The repository includes comprehensive documentation that can be used alongside the book 'Build a Large Language Model (From Scratch)' for additional context and - **Adopt for:** LLMs-from-scratch is a repository that offers detailed, step-by-step guidance on developing, pretraining, and finetuning GPT-like large language models using PyTorch. The codebase complements a book dedicated to building ## Choose when ### Choose Hands-On-Large-Language-Models if… - License: Hands-On-Large-Language-Models is Apache-2.0, LLMs-from-scratch is Other. - Both repositories are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book. - Tags unique to Hands-On-Large-Language-Models: large-language-models, book. - Also covers Developer Tools. - When you seek practical insights into LLMs complemented with nearly 300 custom-made figures for educational clarity; ### Choose LLMs-from-scratch if… - License: LLMs-from-scratch is Other, Hands-On-Large-Language-Models is Apache-2.0. - Requirements: Min 8 GB RAM; The repository includes comprehensive documentation that can be used alongside the book 'Build a Large Language Model (From Scratch)' for additional context and. - Both repositories are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book. - Tags unique to LLMs-from-scratch: deep-learning, ai, instruction-tuning, attention-mechanism. - Also covers Model Training. - When you need detailed, step-by-step explanations and examples for constructing an LLM from scratch with PyTorch. ## When NOT to use Hands-On-Large-Language-Models - If your workflow does not include hands-on coding within Jupyter Notebooks and you do not require the visual educational elements provided by custom figures. - When you need support or solutions using platforms other than Google Colab as setup examples and stability assurances are specifically tailored for Google Colab. - If advanced theoretical insights beyond practical usage of LLMs are your priority, since this tool focuses more on hands-on application rather than deep theory. - In scenarios where immediate access to the latest technical support from a wide community is essential, as this repository’s community might be more niche compared to broader, more generic developer L ## When NOT to use LLMs-from-scratch - When you are looking for a quick setup or already have familiarity with LLMs as the repository emphasizes building from scratch, which can be time-consuming. - If your primary goal is production-scale deployment rather than educational understanding, as this tool focuses more on learning through thoroughness rather than speed and optimization. - For users who prefer not to use specific frameworks like PyTorch and are interested in developing models with other libraries. ## Common questions ### What is the difference between Hands-On-Large-Language-Models and LLMs-from-scratch? Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'. LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose Hands-On-Large-Language-Models over LLMs-from-scratch? Choose Hands-On-Large-Language-Models over LLMs-from-scratch when License: Hands-On-Large-Language-Models is Apache-2.0, LLMs-from-scratch is Other; Both repositories are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book; Tags unique to Hands-On-Large-Language-Models: large-language-models, book; Also covers Developer Tools; When you seek practical insights into LLMs complemented with nearly 300 custom-made figures for educational clarity;. ### When should I choose LLMs-from-scratch over Hands-On-Large-Language-Models? Choose LLMs-from-scratch over Hands-On-Large-Language-Models when License: LLMs-from-scratch is Other, Hands-On-Large-Language-Models is Apache-2.0; Requirements: Min 8 GB RAM; The repository includes comprehensive documentation that can be used alongside the book 'Build a Large Language Model (From Scratch)' for additional context and; Both repositories are focused on hands-on tutorials and implementation of large language models, although 'Hands-On-Large-Language-Models' might provide a broader range of content as it is associated with an O'Reilly book; Tags unique to LLMs-from-scratch: deep-learning, ai, instruction-tuning, attention-mechanism; Also covers Model Training; When you need detailed, step-by-step explanations and examples for constructing an LLM from scratch with PyTorch. ### When should I avoid Hands-On-Large-Language-Models? If your workflow does not include hands-on coding within Jupyter Notebooks and you do not require the visual educational elements provided by custom figures. When you need support or solutions using platforms other than Google Colab as setup examples and stability assurances are specifically tailored for Google Colab. If advanced theoretical insights beyond practical usage of LLMs are your priority, since this tool focuses more on hands-on application rather than deep theory. In scenarios where immediate access to the latest technical support from a wide community is essential, as this repository’s community might be more niche compared to broader, more generic developer L ### When should I avoid LLMs-from-scratch? When you are looking for a quick setup or already have familiarity with LLMs as the repository emphasizes building from scratch, which can be time-consuming. If your primary goal is production-scale deployment rather than educational understanding, as this tool focuses more on learning through thoroughness rather than speed and optimization. For users who prefer not to use specific frameworks like PyTorch and are interested in developing models with other libraries. ### Is Hands-On-Large-Language-Models or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,748 vs 27,427). Stars measure visibility, not whether either tool fits your constraints. ### Are Hands-On-Large-Language-Models and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (Hands-On-Large-Language-Models: Apache-2.0, LLMs-from-scratch: Other). ### Where can I find alternatives to Hands-On-Large-Language-Models or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at /tools/handsonllm-hands-on-large-language-models/alternatives and /tools/rasbt-llms-from-scratch/alternatives (/tools/handsonllm-hands-on-large-language-models/alternatives.md, /tools/rasbt-llms-from-scratch/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 /compare/handsonllm-hands-on-large-language-models-vs-rasbt-llms-from-scratch.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 LLMs-from-scratch? Hands-On-Large-Language-Models: Steady. LLMs-from-scratch: Steady. 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 LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Hands-On-Large-Language-Models: /tools/handsonllm-hands-on-large-language-models/trust; LLMs-from-scratch: /tools/rasbt-llms-from-scratch/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/_