--- title: "FineTuningLLMs vs reasoning-from-scratch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dvgodoy-finetuningllms-vs-rasbt-reasoning-from-scratch" tools: ["dvgodoy-finetuningllms", "rasbt-reasoning-from-scratch"] --- # FineTuningLLMs vs reasoning-from-scratch *GraphCanon updated Aug 17, 2026* ## Verdict Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick reasoning-from-scratch if a step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization. [FineTuningLLMs](https://github.com/dvgodoy/FineTuningLLMs) reports 851 GitHub stars, 114 forks, and 4 open issues, last pushed Feb 28, 2026. [reasoning-from-scratch](https://mng.bz/lZ5B) has 5.0k stars, 759 forks, and 2 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [FineTuningLLMs's repository](https://github.com/dvgodoy/FineTuningLLMs) and [reasoning-from-scratch's repository](https://github.com/rasbt/reasoning-from-scratch). | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | | --- | --- | --- | | Tagline | Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face' | Implement a reasoning LLM in PyTorch from scratch, step by step | | Stars | 851 | 4,998 | | Forks | 114 | 759 | | Open issues | 4 | 2 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. | A step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 License | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [reasoning-from-scratch](/tools/rasbt-reasoning-from-scratch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 146d | 12d | | Open issues (now) | 4 | 2 | | Stars delta | Unknown | +252 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/dvgodoy-finetuningllms/trust.md) | [trust report](/tools/rasbt-reasoning-from-scratch/trust.md) | ## Decision facts: FineTuningLLMs - **Adopt for:** FineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks. ## Decision facts: reasoning-from-scratch - **Requirements:** Automatic GPU utilization where available, though not strictly necessary for the early chapters. - **Adopt for:** A step-by-step guide to building a reasoning large language model (LLM) using PyTorch, suitable for running on consumer hardware with automatic GPU utilization. - **License detail:** Apache-2.0 License ## Choose when ### Choose FineTuningLLMs if… - License: FineTuningLLMs is MIT, reasoning-from-scratch is Apache-2.0. - Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face. - You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem ### Choose reasoning-from-scratch if… - License: reasoning-from-scratch is Apache-2.0, FineTuningLLMs is MIT. - Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.. - Tags unique to reasoning-from-scratch: ai, artificial-intelligence, chain-of-thought, deep-learning. - When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS. ## When NOT to use FineTuningLLMs - Not interested in PyTorch; prefer TensorFlow or another framework - Seek theoretical background over practical applications ## When NOT to use reasoning-from-scratch - Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components. - If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU. ## Common questions ### What is the difference between FineTuningLLMs and reasoning-from-scratch? FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. reasoning-from-scratch: Implement a reasoning LLM in PyTorch from scratch, step by step. See the comparison table for live GitHub stats and shared categories. ### When should I choose FineTuningLLMs over reasoning-from-scratch? Choose FineTuningLLMs over reasoning-from-scratch when License: FineTuningLLMs is MIT, reasoning-from-scratch is Apache-2.0; Tags unique to FineTuningLLMs: bitsandbytes, fine-tuning, finetuning, hugging-face; You need hands-on, step-by-step instructions using PyTorch and the Hugging Face ecosystem. ### When should I choose reasoning-from-scratch over FineTuningLLMs? Choose reasoning-from-scratch over FineTuningLLMs when License: reasoning-from-scratch is Apache-2.0, FineTuningLLMs is MIT; Requirements: Automatic GPU utilization where available, though not strictly necessary for the early chapters.; Tags unique to reasoning-from-scratch: ai, artificial-intelligence, chain-of-thought, deep-learning; When you have intermediate knowledge of PyTorch and want detailed insights into the implementation process of reasoning LLMS. ### When should I avoid FineTuningLLMs? Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications ### When should I avoid reasoning-from-scratch? Avoid this tool if you are looking for rapid prototyping or quick model deployment; it focuses more on understanding and building the LLM from scratch rather than providing prebuilt components. If specialized server hardware is available and preferred for the entire project, as chapters 5 and 6 recommend GPU use but earlier sections can be completed with just a CPU. ### Is FineTuningLLMs or reasoning-from-scratch more popular on GitHub? reasoning-from-scratch has more GitHub stars (4,998 vs 851). Stars measure visibility, not whether either tool fits your constraints. ### Are FineTuningLLMs and reasoning-from-scratch open source? Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, reasoning-from-scratch: Apache-2.0). ### Where can I find alternatives to FineTuningLLMs or reasoning-from-scratch? GraphCanon lists graph-backed alternatives at [FineTuningLLMs alternatives](/tools/dvgodoy-finetuningllms/alternatives) and [reasoning-from-scratch alternatives](/tools/rasbt-reasoning-from-scratch/alternatives) ([FineTuningLLMs markdown twin](/tools/dvgodoy-finetuningllms/alternatives.md), [reasoning-from-scratch markdown twin](/tools/rasbt-reasoning-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/dvgodoy-finetuningllms-vs-rasbt-reasoning-from-scratch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FineTuningLLMs or reasoning-from-scratch? FineTuningLLMs: Slowing. reasoning-from-scratch: 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 FineTuningLLMs and reasoning-from-scratch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FineTuningLLMs trust report](/tools/dvgodoy-finetuningllms/trust); [reasoning-from-scratch trust report](/tools/rasbt-reasoning-from-scratch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dvgodoy-finetuningllms`](/api/graphcanon/graph?tool=dvgodoy-finetuningllms) - 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/_