--- title: "llm_note vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/harleyszhang-llm-note-vs-rasbt-llms-from-scratch" tools: ["harleyszhang-llm-note", "rasbt-llms-from-scratch"] --- # llm_note vs LLMs-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm_note when llm_note is primarily Python; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; llm_note is Python. [llm_note](https://github.com/harleyszhang/llm_note) reports 882 GitHub stars, 88 forks, and 0 open issues, last pushed Jul 2, 2026. [LLMs-from-scratch](https://amzn.to/4fqvn0D) has 99k stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [llm_note's repository](https://github.com/harleyszhang/llm_note) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [llm_note](/tools/harleyszhang-llm-note.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | LLM notes, including model inference, transformer model structure, and llm framework code analysis notes. | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 882 | 98,899 | | Forks | 88 | 15,183 | | Open issues | 0 | 4 | | Language | Python | Jupyter Notebook | | Adopt for | - | LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. | | Persona | - | - | | Runtime | - | - | | License | - | Other | | Categories | LLM Frameworks, Model Training, Inference & Serving | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm_note](/tools/harleyszhang-llm-note.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 8d | 38d | | Open issues (now) | 0 | 4 | | Full report | [trust report](/tools/harleyszhang-llm-note/trust.md) | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | ## Decision facts: LLMs-from-scratch - **Adopt for:** LLMs-from-scratch is a project-oriented repository aimed at building PyTorch-based language models from the ground up, with detailed step-by-step instructions. ## Choose when ### Choose llm_note if… - llm_note is primarily Python; LLMs-from-scratch is Jupyter Notebook. - Tags unique to llm_note: cuda-programming, transformer-models, triton-kernels, llm. - Also covers Inference & Serving. ### Choose LLMs-from-scratch if… - LLMs-from-scratch is primarily Jupyter Notebook; llm_note is Python. - Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## When NOT to use llm_note - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## When NOT to use LLMs-from-scratch - - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 - a deeper learning experience. ## Common questions ### What is the difference between llm_note and LLMs-from-scratch? llm_note: LLM notes, including model inference, transformer model structure, and llm framework code analysis notes.. 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 llm_note over LLMs-from-scratch? Choose llm_note over LLMs-from-scratch when llm_note is primarily Python; LLMs-from-scratch is Jupyter Notebook; Tags unique to llm_note: cuda-programming, transformer-models, triton-kernels, llm; Also covers Inference & Serving. ### When should I choose LLMs-from-scratch over llm_note? Choose LLMs-from-scratch over llm_note when LLMs-from-scratch is primarily Jupyter Notebook; llm_note is Python; Tags unique to LLMs-from-scratch: deep-learning, ai, artificial-intelligence, attention-mechanism; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I avoid llm_note? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### When should I avoid LLMs-from-scratch? - If you are looking for a rapid deployment of an LLM without understanding its intricate structure - this tool requires extensive manual and conceptual work. - You prefer frameworks with automatic model generation or other high-level abstractions that simplify the process. This repository emphasizes manual creation, which is more time-consuming but offers丰 a deeper learning experience. ### Is llm_note or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 882). Stars measure visibility, not whether either tool fits your constraints. ### Are llm_note and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to llm_note or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [llm_note alternatives](/tools/harleyszhang-llm-note/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([llm_note markdown twin](/tools/harleyszhang-llm-note/alternatives.md), [LLMs-from-scratch markdown twin](/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 [this comparison](/compare/harleyszhang-llm-note-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, llm_note or LLMs-from-scratch? llm_note: Active. 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 llm_note and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm_note trust report](/tools/harleyszhang-llm-note/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=harleyszhang-llm-note`](/api/graphcanon/graph?tool=harleyszhang-llm-note) - 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/_