--- title: "octopack vs LLMs-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-octopack-vs-rasbt-llms-from-scratch" tools: ["bigcode-project-octopack", "rasbt-llms-from-scratch"] --- # octopack vs LLMs-from-scratch *GraphCanon updated Jul 11, 2026* ## Verdict Pick octopack when license: octopack is MIT, LLMs-from-scratch is Other; pick LLMs-from-scratch when license: LLMs-from-scratch is Other, octopack is MIT. [octopack](https://arxiv.org/abs/2308.07124) reports 479 GitHub stars, 29 forks, and 14 open issues, last pushed Feb 5, 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 [octopack's repository](https://github.com/bigcode-project/octopack) and [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch). | | [octopack](/tools/bigcode-project-octopack.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Tagline | 🐙 OctoPack: Instruction Tuning Code Large Language Models | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | | Stars | 479 | 98,899 | | Forks | 29 | 15,183 | | Open issues | 14 | 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, Vector Databases | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [octopack](/tools/bigcode-project-octopack.md) | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 521d | 38d | | Open issues (now) | 14 | 4 | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-octopack/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 octopack if… - License: octopack is MIT, LLMs-from-scratch is Other. - Tags unique to octopack: jupyter notebook. - Also covers Vector Databases. ### Choose LLMs-from-scratch if… - License: LLMs-from-scratch is Other, octopack 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 octopack - Last GitHub push was 521 days ago (dormant maintenance, Feb 5, 2025). Validate activity before betting a new project on octopack. - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 octopack and LLMs-from-scratch? octopack: 🐙 OctoPack: Instruction Tuning Code 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 octopack over LLMs-from-scratch? Choose octopack over LLMs-from-scratch when License: octopack is MIT, LLMs-from-scratch is Other; Tags unique to octopack: jupyter notebook; Also covers Vector Databases. ### When should I choose LLMs-from-scratch over octopack? Choose LLMs-from-scratch over octopack when License: LLMs-from-scratch is Other, octopack 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 octopack? Last GitHub push was 521 days ago (dormant maintenance, Feb 5, 2025). Validate activity before betting a new project on octopack. 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 octopack or LLMs-from-scratch more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 479). Stars measure visibility, not whether either tool fits your constraints. ### Are octopack and LLMs-from-scratch open source? Yes - both are open-source projects on GitHub (octopack: MIT, LLMs-from-scratch: Other). ### Where can I find alternatives to octopack or LLMs-from-scratch? GraphCanon lists graph-backed alternatives at [octopack alternatives](/tools/bigcode-project-octopack/alternatives) and [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) ([octopack markdown twin](/tools/bigcode-project-octopack/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/bigcode-project-octopack-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, octopack or LLMs-from-scratch? octopack: 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 octopack and LLMs-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [octopack trust report](/tools/bigcode-project-octopack/trust); [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigcode-project-octopack`](/api/graphcanon/graph?tool=bigcode-project-octopack) - 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/_