--- title: "FineTuningLLMs vs Hands-On-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dvgodoy-finetuningllms-vs-handsonllm-hands-on-large-language-models" tools: ["dvgodoy-finetuningllms", "handsonllm-hands-on-large-language-models"] --- # FineTuningLLMs vs Hands-On-Large-Language-Models *GraphCanon updated Aug 24, 2026* ## Verdict Pick FineTuningLLMs if fineTuningLLMs is designed for users familiar with PyTorch and Hugging Face who seek practical guidance via Jupyter Notebooks; pick Hands-On-Large-Language-Models if consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples. [FineTuningLLMs](https://github.com/dvgodoy/FineTuningLLMs) reports 855 GitHub stars, 116 forks, and 4 open issues, last pushed Feb 28, 2026. [Hands-On-Large-Language-Models](https://www.llm-book.com/) has 28k stars, 6.5k forks, and 38 open issues, last pushed Apr 24, 2026. Figures are from public GitHub metadata via [FineTuningLLMs's repository](https://github.com/dvgodoy/FineTuningLLMs) and [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models). | | [FineTuningLLMs](/tools/dvgodoy-finetuningllms.md) | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | | --- | --- | --- | | Tagline | Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face' | Official code repo for the O'Reilly Book - 'Hands-On Large Language Models' | | Stars | 855 | 28,252 | | Forks | 116 | 6,531 | | Open issues | 4 | 38 | | 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. | Consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples. | | 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) | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | | --- | --- | --- | | Days since push | 176d | 114d | | Open issues (now) | 4 | 38 | | Stars delta | +4 (30d) | +642 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/dvgodoy-finetuningllms/trust.md) | [trust report](/tools/handsonllm-hands-on-large-language-models/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: Hands-On-Large-Language-Models - **Pricing:** freemium - The repository is free and open under the Apache-2.0 license. - **Requirements:** - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial. - **Adopt for:** Consider using the 'Hands-On-Large-Language-Models' repository if your interest aligns with hands-on learning and practice of large language models through coding examples. - **License detail:** Apache-2.0 License ## Choose when ### Choose FineTuningLLMs if… - License: FineTuningLLMs is MIT, Hands-On-Large-Language-Models 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 Hands-On-Large-Language-Models if… - License: Hands-On-Large-Language-Models is Apache-2.0, FineTuningLLMs is MIT. - Pricing: The repository is free and open under the Apache-2.0 license.. - Requirements: - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial.. - Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llm, llms. - - You are focusing on practical implementation aspects detailed in a structured format as outlined by O'Reilly's authoritative book. ## When NOT to use FineTuningLLMs - Not interested in PyTorch; prefer TensorFlow or another framework - Seek theoretical background over practical applications ## When NOT to use Hands-On-Large-Language-Models - - If you need real-time model evaluation tools rather than educational materials, as this repository primarily provides code for understanding and implementing concepts covered in a book. - - You are seeking proprietary or more specialized frameworks that go beyond the examples provided in an educational context to meet specific, advanced use-case needs. ## Common questions ### What is the difference between FineTuningLLMs and Hands-On-Large-Language-Models? FineTuningLLMs: Official repository for 'A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face'. Hands-On-Large-Language-Models: Official code repo for the O'Reilly Book - 'Hands-On Large Language Models'. See the comparison table for live GitHub stats and shared categories. ### When should I choose FineTuningLLMs over Hands-On-Large-Language-Models? Choose FineTuningLLMs over Hands-On-Large-Language-Models when License: FineTuningLLMs is MIT, Hands-On-Large-Language-Models 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 Hands-On-Large-Language-Models over FineTuningLLMs? Choose Hands-On-Large-Language-Models over FineTuningLLMs when License: Hands-On-Large-Language-Models is Apache-2.0, FineTuningLLMs is MIT; Pricing: The repository is free and open under the Apache-2.0 license.; Requirements: - Access to Jupyter Notebook is required for running code examples provided in this repository.; - Fundamental understanding of large language models and familiarity with AI concepts would be beneficial.; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llm, llms; - You are focusing on practical implementation aspects detailed in a structured format as outlined by O'Reilly's authoritative book. ### When should I avoid FineTuningLLMs? Not interested in PyTorch; prefer TensorFlow or another framework Seek theoretical background over practical applications ### When should I avoid Hands-On-Large-Language-Models? - If you need real-time model evaluation tools rather than educational materials, as this repository primarily provides code for understanding and implementing concepts covered in a book. - You are seeking proprietary or more specialized frameworks that go beyond the examples provided in an educational context to meet specific, advanced use-case needs. ### Is FineTuningLLMs or Hands-On-Large-Language-Models more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (28,252 vs 855). Stars measure visibility, not whether either tool fits your constraints. ### Are FineTuningLLMs and Hands-On-Large-Language-Models open source? Yes - both are open-source projects on GitHub (FineTuningLLMs: MIT, Hands-On-Large-Language-Models: Apache-2.0). ### Where can I find alternatives to FineTuningLLMs or Hands-On-Large-Language-Models? GraphCanon lists graph-backed alternatives at [FineTuningLLMs alternatives](/tools/dvgodoy-finetuningllms/alternatives) and [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) ([FineTuningLLMs markdown twin](/tools/dvgodoy-finetuningllms/alternatives.md), [Hands-On-Large-Language-Models markdown twin](/tools/handsonllm-hands-on-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/dvgodoy-finetuningllms-vs-handsonllm-hands-on-large-language-models.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FineTuningLLMs or Hands-On-Large-Language-Models? FineTuningLLMs: Slowing. Hands-On-Large-Language-Models: Slowing. 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 Hands-On-Large-Language-Models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FineTuningLLMs trust report](/tools/dvgodoy-finetuningllms/trust); [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/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/_