--- title: "LLM-Adapters vs Hands-On-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-edgerunners-llm-adapters-vs-handsonllm-hands-on-large-language-models" tools: ["agi-edgerunners-llm-adapters", "handsonllm-hands-on-large-language-models"] --- # LLM-Adapters vs Hands-On-Large-Language-Models *GraphCanon updated Aug 24, 2026* ## Verdict Pick LLM-Adapters if lLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing; 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. [LLM-Adapters](https://arxiv.org/abs/2304.01933) reports 1.2k GitHub stars, 115 forks, and 55 open issues, last pushed Mar 10, 2024. [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 [LLM-Adapters's repository](https://github.com/AGI-Edgerunners/LLM-Adapters) and [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models). | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | | --- | --- | --- | | Tagline | Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs | Official code repo for the O'Reilly Book - 'Hands-On Large Language Models' | | Stars | 1,233 | 28,252 | | Forks | 115 | 6,531 | | Open issues | 55 | 38 | | Language | Python | Jupyter Notebook | | Adopt for | LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. | 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 | Apache-2.0 | 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._ | | [LLM-Adapters](/tools/agi-edgerunners-llm-adapters.md) | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 896d | 114d | | Open issues (now) | 55 | 38 | | Stars delta | -1 (30d) | +642 (30d) | | Full report | [trust report](/tools/agi-edgerunners-llm-adapters/trust.md) | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | ## Decision facts: LLM-Adapters - **Adopt for:** LLM-Adapters offers Python-based tools for efficient fine-tuning of language models with Apache-2.0 licensing. ## 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 LLM-Adapters if… - LLM-Adapters is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook. - Tags unique to LLM-Adapters: adapters, fine-tuning, parameter-efficient. - Optimizing resource usage when you need to fine-tune large language models without altering their core parameters ### Choose Hands-On-Large-Language-Models if… - Hands-On-Large-Language-Models is primarily Jupyter Notebook; LLM-Adapters is Python. - 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 LLM-Adapters - You require a full retraining approach that modifies all model weights, not just adapters - Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023 ## 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 LLM-Adapters and Hands-On-Large-Language-Models? LLM-Adapters: Code for EMNLP 2023 Paper on Parameter-Efficient Fine-Tuning of LLMs. 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 LLM-Adapters over Hands-On-Large-Language-Models? Choose LLM-Adapters over Hands-On-Large-Language-Models when LLM-Adapters is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook; Tags unique to LLM-Adapters: adapters, fine-tuning, parameter-efficient; Optimizing resource usage when you need to fine-tune large language models without altering their core parameters. ### When should I choose Hands-On-Large-Language-Models over LLM-Adapters? Choose Hands-On-Large-Language-Models over LLM-Adapters when Hands-On-Large-Language-Models is primarily Jupyter Notebook; LLM-Adapters is Python; 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 LLM-Adapters? You require a full retraining approach that modifies all model weights, not just adapters Your project timeline does not allow for integrating and testing new methodologies from recent papers like EMNLP 2023 ### 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 LLM-Adapters or Hands-On-Large-Language-Models more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (28,252 vs 1,233). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM-Adapters and Hands-On-Large-Language-Models open source? Yes - both are open-source projects on GitHub (LLM-Adapters: Apache-2.0, Hands-On-Large-Language-Models: Apache-2.0). ### Where can I find alternatives to LLM-Adapters or Hands-On-Large-Language-Models? GraphCanon lists graph-backed alternatives at [LLM-Adapters alternatives](/tools/agi-edgerunners-llm-adapters/alternatives) and [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) ([LLM-Adapters markdown twin](/tools/agi-edgerunners-llm-adapters/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/agi-edgerunners-llm-adapters-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, LLM-Adapters or Hands-On-Large-Language-Models? LLM-Adapters: Dormant. 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 LLM-Adapters and Hands-On-Large-Language-Models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM-Adapters trust report](/tools/agi-edgerunners-llm-adapters/trust); [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters`](/api/graphcanon/graph?tool=agi-edgerunners-llm-adapters) - 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/_