--- title: "dart-math vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hkust-nlp-dart-math-vs-rasbt-llms-from-scratch" tools: ["hkust-nlp-dart-math", "rasbt-llms-from-scratch"] --- # dart-math vs LLMs-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick dart-math when license: dart-math is MIT, LLMs-from-scratch is Other; pick LLMs-from-scratch when license: LLMs-from-scratch is Other, dart-math is MIT. [dart-math](https://hkust-nlp.github.io/dart-math/) reports 120 GitHub stars, 8 forks, and 5 open issues, last pushed Dec 10, 2024. [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 [dart-math's repository](https://github.com/hkust-nlp/dart-math) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [dart-math](/tools/hkust-nlp-dart-math.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | [NeurIPS'24] Official code for *🎯DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving* | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 120 | 98,899 | | Forks | 8 | 15,183 | | Open issues | 5 | 4 | | Language | Jupyter Notebook | 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 | MIT | Other | | Categories | LLM Frameworks, Model Training, Inference & Serving | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dart-math](/tools/hkust-nlp-dart-math.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 578d | 38d | | Open issues (now) | 5 | 4 | | Owner type | Organization | User | | Security scan | No criticals | No lockfile | | Full report | [trust report](/tools/hkust-nlp-dart-math/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 dart-math if… - License: dart-math is MIT, LLMs-from-scratch is Other. - Tags unique to dart-math: llm, nlp, jupyter notebook, llm-inference. - Also covers Inference & Serving. ### Choose LLMs-from-scratch if… - License: LLMs-from-scratch is Other, dart-math is MIT. - Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, from-scratch. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## When NOT to use dart-math - Last GitHub push was 579 days ago (dormant maintenance, Dec 10, 2024). Validate activity before betting a new project on dart-math. - 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 dart-math and LLMs-from-scratch? dart-math: [NeurIPS'24] Official code for *🎯DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving*. 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 dart-math over LLMs-from-scratch? Choose dart-math over LLMs-from-scratch when License: dart-math is MIT, LLMs-from-scratch is Other; Tags unique to dart-math: llm, nlp, jupyter notebook, llm-inference; Also covers Inference & Serving. ### When should I choose LLMs-from-scratch over dart-math? Choose LLMs-from-scratch over dart-math when License: LLMs-from-scratch is Other, dart-math is MIT; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, from-scratch; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I avoid dart-math? Last GitHub push was 579 days ago (dormant maintenance, Dec 10, 2024). Validate activity before betting a new project on dart-math. 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 dart-math or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 120). Stars measure visibility, not whether either tool fits your constraints. ### Are dart-math and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (dart-math: MIT, LLMs-from-scratch: Other). ### Where can I find alternatives to dart-math or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [dart-math alternatives](/tools/hkust-nlp-dart-math/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([dart-math markdown twin](/tools/hkust-nlp-dart-math/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/hkust-nlp-dart-math-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, dart-math or LLMs-from-scratch? dart-math: Dormant. 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 dart-math and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dart-math trust report](/tools/hkust-nlp-dart-math/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hkust-nlp-dart-math`](/api/graphcanon/graph?tool=hkust-nlp-dart-math) - 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/_