--- title: "LLMs-from-scratch vs dvc" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rasbt-llms-from-scratch-vs-treeverse-dvc" tools: ["rasbt-llms-from-scratch", "treeverse-dvc"] --- # LLMs-from-scratch vs dvc *GraphCanon updated Jul 12, 2026* ## Verdict Pick LLMs-from-scratch when lLMs-from-scratch is primarily Jupyter Notebook; dvc is Python; pick dvc when dvc is primarily Python; LLMs-from-scratch is Jupyter Notebook. [LLMs-from-scratch](https://amzn.to/4fqvn0D) reports 99k GitHub stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. [dvc](https://dvc.org) has 16k stars, 1.3k forks, and 188 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch) and [dvc's repository](https://github.com/treeverse/dvc). | | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | [dvc](/tools/treeverse-dvc.md) | | --- | --- | --- | | Tagline | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | Data Versioning and ML Experiments | | Stars | 98,899 | 15,740 | | Forks | 15,183 | 1,312 | | Open issues | 4 | 188 | | Language | Jupyter Notebook | Python | | 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 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | [dvc](/tools/treeverse-dvc.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 38d | 1d | | Open issues (now) | 4 | 188 | | Owner type | User | Organization | | Full report | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | [trust report](/tools/treeverse-dvc/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 LLMs-from-scratch if… - LLMs-from-scratch is primarily Jupyter Notebook; dvc is Python. - License: LLMs-from-scratch is Other, dvc is Apache-2.0. - Tags unique to LLMs-from-scratch: artificial-intelligence, attention mechanism, deep-learning, finetuning. - Also covers LLM Frameworks. - - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### Choose dvc if… - dvc is primarily Python; LLMs-from-scratch is Jupyter Notebook. - License: dvc is Apache-2.0, LLMs-from-scratch is Other. - Tags unique to dvc: data-science, reproducibility, unstructured-data. - Also covers Developer Tools. ## 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. ## When NOT to use dvc - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between LLMs-from-scratch and dvc? LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. dvc: Data Versioning and ML Experiments. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMs-from-scratch over dvc? Choose LLMs-from-scratch over dvc when LLMs-from-scratch is primarily Jupyter Notebook; dvc is Python; License: LLMs-from-scratch is Other, dvc is Apache-2.0; Tags unique to LLMs-from-scratch: artificial-intelligence, attention mechanism, deep-learning, finetuning; Also covers LLM Frameworks; - You are an advanced practitioner aiming to fully understand the underpinnings of LLMs using PyTorch as your primary framework. ### When should I choose dvc over LLMs-from-scratch? Choose dvc over LLMs-from-scratch when dvc is primarily Python; LLMs-from-scratch is Jupyter Notebook; License: dvc is Apache-2.0, LLMs-from-scratch is Other; Tags unique to dvc: data-science, reproducibility, unstructured-data; Also covers Developer Tools. ### 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. ### When should I avoid dvc? Developer Tools: A gateway is overkill when you're pinned to a single provider and model. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is LLMs-from-scratch or dvc more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 15,740). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMs-from-scratch and dvc open source? Yes - both are open-source projects on GitHub (LLMs-from-scratch: Other, dvc: Apache-2.0). ### Where can I find alternatives to LLMs-from-scratch or dvc? GraphCanon lists graph-backed alternatives at [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) and [dvc alternatives](/tools/treeverse-dvc/alternatives) ([LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/alternatives.md), [dvc markdown twin](/tools/treeverse-dvc/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/rasbt-llms-from-scratch-vs-treeverse-dvc.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMs-from-scratch or dvc? LLMs-from-scratch: Steady. dvc: Very active. 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 LLMs-from-scratch and dvc? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust); [dvc trust report](/tools/treeverse-dvc/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rasbt-llms-from-scratch`](/api/graphcanon/graph?tool=rasbt-llms-from-scratch) - 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/_