--- title: "LLMs-from-scratch vs LLM-FineTuning-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rasbt-llms-from-scratch-vs-rohan-paul-llm-finetuning-large-language-models" tools: ["rasbt-llms-from-scratch", "rohan-paul-llm-finetuning-large-language-models"] --- # LLMs-from-scratch vs LLM-FineTuning-Large-Language-Models *GraphCanon updated Jul 11, 2026* ## Verdict Pick LLMs-from-scratch when tags unique to LLMs-from-scratch: ai, artificial-intelligence, attention mechanism, deep-learning; pick LLM-FineTuning-Large-Language-Models when tags unique to LLM-FineTuning-Large-Language-Models: gpt-3, gpt3-turbo, large-language-models, llama2. [LLMs-from-scratch](https://amzn.to/4fqvn0D) reports 99k GitHub stars, 15k forks, and 4 open issues, last pushed Jun 2, 2026. [LLM-FineTuning-Large-Language-Models](https://github.com/rohan-paul/LLM-FineTuning-Large-Language-Models) has 576 stars, 140 forks, and 2 open issues, last pushed Apr 1, 2025. Figures are from public GitHub metadata via [LLMs-from-scratch's repository](https://github.com/rasbt/LLMs-from-scratch) and [LLM-FineTuning-Large-Language-Models's repository](https://github.com/rohan-paul/LLM-FineTuning-Large-Language-Models). | | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | [LLM-FineTuning-Large-Language-Models](/tools/rohan-paul-llm-finetuning-large-language-models.md) | | --- | --- | --- | | Tagline | Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | LLM (Large Language Model) FineTuning | | Stars | 98,899 | 576 | | Forks | 15,183 | 140 | | Open issues | 4 | 2 | | 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 | Other | - | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) | [LLM-FineTuning-Large-Language-Models](/tools/rohan-paul-llm-finetuning-large-language-models.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 38d | 465d | | Open issues (now) | 4 | 2 | | Full report | [trust report](/tools/rasbt-llms-from-scratch/trust.md) | [trust report](/tools/rohan-paul-llm-finetuning-large-language-models/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… - 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. - More GitHub stars (99k vs 576) - visibility, not fit. ### Choose LLM-FineTuning-Large-Language-Models if… - Tags unique to LLM-FineTuning-Large-Language-Models: gpt-3, gpt3-turbo, large-language-models, llama2. - Also covers Inference & Serving. - Leaner open-issue backlog (2). ## 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 LLM-FineTuning-Large-Language-Models - Last GitHub push was 466 days ago (dormant maintenance, Apr 1, 2025). Validate activity before betting a new project on LLM-FineTuning-Large-Language-Models. - 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. ## Common questions ### What is the difference between LLMs-from-scratch and LLM-FineTuning-Large-Language-Models? LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step. LLM-FineTuning-Large-Language-Models: LLM (Large Language Model) FineTuning. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMs-from-scratch over LLM-FineTuning-Large-Language-Models? Choose LLMs-from-scratch over LLM-FineTuning-Large-Language-Models when 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; More GitHub stars (99k vs 576) - visibility, not fit. ### When should I choose LLM-FineTuning-Large-Language-Models over LLMs-from-scratch? Choose LLM-FineTuning-Large-Language-Models over LLMs-from-scratch when Tags unique to LLM-FineTuning-Large-Language-Models: gpt-3, gpt3-turbo, large-language-models, llama2; Also covers Inference & Serving; Leaner open-issue backlog (2). ### 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 LLM-FineTuning-Large-Language-Models? Last GitHub push was 466 days ago (dormant maintenance, Apr 1, 2025). Validate activity before betting a new project on LLM-FineTuning-Large-Language-Models. 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. ### Is LLMs-from-scratch or LLM-FineTuning-Large-Language-Models more popular on GitHub? LLMs-from-scratch has more GitHub stars (98,899 vs 576). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMs-from-scratch and LLM-FineTuning-Large-Language-Models open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMs-from-scratch or LLM-FineTuning-Large-Language-Models? GraphCanon lists graph-backed alternatives at [LLMs-from-scratch alternatives](/tools/rasbt-llms-from-scratch/alternatives) and [LLM-FineTuning-Large-Language-Models alternatives](/tools/rohan-paul-llm-finetuning-large-language-models/alternatives) ([LLMs-from-scratch markdown twin](/tools/rasbt-llms-from-scratch/alternatives.md), [LLM-FineTuning-Large-Language-Models markdown twin](/tools/rohan-paul-llm-finetuning-large-language-models/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-rohan-paul-llm-finetuning-large-language-models.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 LLM-FineTuning-Large-Language-Models? LLMs-from-scratch: Steady. LLM-FineTuning-Large-Language-Models: Dormant. 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 LLM-FineTuning-Large-Language-Models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMs-from-scratch trust report](/tools/rasbt-llms-from-scratch/trust); [LLM-FineTuning-Large-Language-Models trust report](/tools/rohan-paul-llm-finetuning-large-language-models/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/_