--- title: "react-native-transformers vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/daviddaytw-react-native-transformers-vs-rasbt-llms-from-scratch" tools: ["daviddaytw-react-native-transformers", "rasbt-llms-from-scratch"] --- # react-native-transformers vs LLMs-from-scratch *GraphCanon updated Jul 15, 2026* ## Verdict Pick react-native-transformers when react-native-transformers is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook; pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; react-native-transformers is TypeScript. [react-native-transformers](https://www.npmjs.com/package/react-native-transformers) reports 133 GitHub stars, 16 forks, and 7 open issues, last pushed Jul 13, 2025. [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 [react-native-transformers's repository](https://github.com/daviddaytw/react-native-transformers) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [react-native-transformers](/tools/daviddaytw-react-native-transformers.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | Run local LLM from Huggingface in React-Native or Expo using onnxruntime. | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 133 | 98,899 | | Forks | 16 | 15,183 | | Open issues | 7 | 4 | | Language | TypeScript | 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 | Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [react-native-transformers](/tools/daviddaytw-react-native-transformers.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 367d | 38d | | Open issues (now) | 7 | 4 | | Full report | [trust report](/tools/daviddaytw-react-native-transformers/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 react-native-transformers if… - react-native-transformers is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook. - License: react-native-transformers is MIT, LLMs-from-scratch is Other. - Tags unique to react-native-transformers: expo, huggingface, local-llm, onnx. - Also covers Inference & Serving. ### Choose LLMs-from-scratch if… - LLMs-from-scratch is primarily Jupyter Notebook; react-native-transformers is TypeScript. - License: LLMs-from-scratch is Other, react-native-transformers is MIT. - Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, deep-learning. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ## When NOT to use react-native-transformers - Last GitHub push was 367 days ago (dormant maintenance, Jul 13, 2025). Validate activity before betting a new project on react-native-transformers. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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. ## 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 - a deeper learning experience. ## Common questions ### What is the difference between react-native-transformers and LLMs-from-scratch? react-native-transformers: Run local LLM from Huggingface in React-Native or Expo using onnxruntime.. 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 react-native-transformers over LLMs-from-scratch? Choose react-native-transformers over LLMs-from-scratch when react-native-transformers is primarily TypeScript; LLMs-from-scratch is Jupyter Notebook; License: react-native-transformers is MIT, LLMs-from-scratch is Other; Tags unique to react-native-transformers: expo, huggingface, local-llm, onnx; Also covers Inference & Serving. ### When should I choose LLMs-from-scratch over react-native-transformers? Choose LLMs-from-scratch over react-native-transformers when LLMs-from-scratch is primarily Jupyter Notebook; react-native-transformers is TypeScript; License: LLMs-from-scratch is Other, react-native-transformers is MIT; Tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention-mechanism, deep-learning; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I avoid react-native-transformers? Last GitHub push was 367 days ago (dormant maintenance, Jul 13, 2025). Validate activity before betting a new project on react-native-transformers. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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. ### 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 a deeper learning experience. ### Is react-native-transformers or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 133). Stars measure visibility, not whether either tool fits your constraints. ### Are react-native-transformers and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (react-native-transformers: MIT, LLMs-from-scratch: Other). ### Where can I find alternatives to react-native-transformers or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [react-native-transformers alternatives](/tools/daviddaytw-react-native-transformers/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([react-native-transformers markdown twin](/tools/daviddaytw-react-native-transformers/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/daviddaytw-react-native-transformers-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, react-native-transformers or LLMs-from-scratch? react-native-transformers: 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 react-native-transformers and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [react-native-transformers trust report](/tools/daviddaytw-react-native-transformers/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=daviddaytw-react-native-transformers`](/api/graphcanon/graph?tool=daviddaytw-react-native-transformers) - 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/_