--- title: "LLM4Decompile vs Hands-On-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/albertan017-llm4decompile-vs-handsonllm-hands-on-large-language-models" tools: ["albertan017-llm4decompile", "handsonllm-hands-on-large-language-models"] --- # LLM4Decompile vs Hands-On-Large-Language-Models *GraphCanon updated Sep 20, 2026* ## Verdict Pick LLM4Decompile if lLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code; pick Hands-On-Large-Language-Models if hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵. [LLM4Decompile](https://aclanthology.org/2024.emnlp-main.203) reports 7.0k GitHub stars, 546 forks, and 46 open issues, last pushed Feb 12, 2026. [Hands-On-Large-Language-Models](https://www.llm-book.com/) has 29k stars, 6.7k forks, and 40 open issues, last pushed Apr 24, 2026. Figures are from public GitHub metadata via [LLM4Decompile's repository](https://github.com/albertan017/LLM4Decompile) and [Hands-On-Large-Language-Models's repository](https://github.com/HandsOnLLM/Hands-On-Large-Language-Models). | | [LLM4Decompile](/tools/albertan017-llm4decompile.md) | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | | --- | --- | --- | | Tagline | Decompiling Binary Code with Large Language Models | Code for the book 'Hands-On Large Language Models' | | Stars | 6,965 | 29,162 | | Forks | 546 | 6,662 | | Open issues | 46 | 40 | | Language | Python | Jupyter Notebook | | Adopt for | LLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code. | Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵 | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | LLM Frameworks | Developer Tools, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM4Decompile](/tools/albertan017-llm4decompile.md) | [Hands-On-Large-Language-Models](/tools/handsonllm-hands-on-large-language-models.md) | | --- | --- | --- | | Days since push | 186d | 146d | | Open issues (now) | 46 | 40 | | Stars delta | +205 (30d) | +910 (30d) | | Open issues delta | 0 (30d) | +2 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/albertan017-llm4decompile/trust.md) | [trust report](/tools/handsonllm-hands-on-large-language-models/trust.md) | **Typed relationship:** LLM4Decompile _(related)_ Hands-On-Large-Language-Models Both projects are related to large language models, with Hands-On-Large-Language-Models focusing on educational materials and LLM4Decompile applying LLMs in the context of reverse engineering. ## Decision facts: LLM4Decompile - **Pricing:** freemium - The tool itself is open-source under the MIT license, but using it effectively may require access to specific large language models that could have associated costs. - **Requirements:** Min 16 GB RAM; Requires a GPU for optimal performance with the specified model. - **Adopt for:** LLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code. ## Decision facts: Hands-On-Large-Language-Models - **Adopt for:** Hands-On-Large-Language-Models offers code examples and educational content for large language models, as detailed in the O'Reilly book 'Hands-On Large Language Models'. The repository is in Jupyter Notebook format and,遵 ## Choose when ### Choose LLM4Decompile if… - LLM4Decompile is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook. - License: LLM4Decompile is MIT, Hands-On-Large-Language-Models is Apache-2.0. - Pricing: The tool itself is open-source under the MIT license, but using it effectively may require access to specific large language models that could have associated costs.. - Requirements: Min 16 GB RAM; Requires a GPU for optimal performance with the specified model.. - Both projects are related to large language models, with Hands-On-Large-Language-Models focusing on educational materials and LLM4Decompile applying LLMs in the context of reverse engineering. - Tags unique to LLM4Decompile: binary, decompile, reverse-engineering. - LLM4Decompile ships Docker support for self-hosted deployment. - When you need a tool that leverages advanced language models for decompiling binaries more effectively than traditional methods. ### Choose Hands-On-Large-Language-Models if… - Hands-On-Large-Language-Models is primarily Jupyter Notebook; LLM4Decompile is Python. - License: Hands-On-Large-Language-Models is Apache-2.0, LLM4Decompile is MIT. - Both projects are related to large language models, with Hands-On-Large-Language-Models focusing on educational materials and LLM4Decompile applying LLMs in the context of reverse engineering. - Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llm, llms. - Also covers Developer Tools. - Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'. ## When NOT to use LLM4Decompile - Avoid this tool if you require high precision in recreating exact source code, especially for heavily optimized binaries that lose contextual information during compilation. - Do not use LLM4Decompile when working with less common architectures (e.g., RISC-V) unless explicitly supported or tested by the model. ## When NOT to use Hands-On-Large-Language-Models - Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. - Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. - If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit. ## Common questions ### What is the difference between LLM4Decompile and Hands-On-Large-Language-Models? LLM4Decompile: Decompiling Binary Code with Large Language Models. Hands-On-Large-Language-Models: Code for the book 'Hands-On Large Language Models'. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLM4Decompile over Hands-On-Large-Language-Models? Choose LLM4Decompile over Hands-On-Large-Language-Models when LLM4Decompile is primarily Python; Hands-On-Large-Language-Models is Jupyter Notebook; License: LLM4Decompile is MIT, Hands-On-Large-Language-Models is Apache-2.0; Pricing: The tool itself is open-source under the MIT license, but using it effectively may require access to specific large language models that could have associated costs.; Requirements: Min 16 GB RAM; Requires a GPU for optimal performance with the specified model.; Both projects are related to large language models, with Hands-On-Large-Language-Models focusing on educational materials and LLM4Decompile applying LLMs in the context of reverse engineering; Tags unique to LLM4Decompile: binary, decompile, reverse-engineering; LLM4Decompile ships Docker support for self-hosted deployment; When you need a tool that leverages advanced language models for decompiling binaries more effectively than traditional methods. ### When should I choose Hands-On-Large-Language-Models over LLM4Decompile? Choose Hands-On-Large-Language-Models over LLM4Decompile when Hands-On-Large-Language-Models is primarily Jupyter Notebook; LLM4Decompile is Python; License: Hands-On-Large-Language-Models is Apache-2.0, LLM4Decompile is MIT; Both projects are related to large language models, with Hands-On-Large-Language-Models focusing on educational materials and LLM4Decompile applying LLMs in the context of reverse engineering; Tags unique to Hands-On-Large-Language-Models: artificial-intelligence, book, llm, llms; Also covers Developer Tools; Use this tool if you are looking for practical, code-based learning resources that align with the book 'Hands-On Large Language Models'. ### When should I avoid LLM4Decompile? Avoid this tool if you require high precision in recreating exact source code, especially for heavily optimized binaries that lose contextual information during compilation. Do not use LLM4Decompile when working with less common architectures (e.g., RISC-V) unless explicitly supported or tested by the model. ### When should I avoid Hands-On-Large-Language-Models? Avoid this tool if you are seeking a comprehensive theoretical background without practical examples, as the focus is on hands-on learning. Not recommended for users who prefer a different platform for running code examples, as the repository is optimized for Google Colab with a T4 GPU and 16GB VRAM. If you are looking for a tool that covers a broader range of AI topics beyond large language models, this repository may not be the best fit. ### Is LLM4Decompile or Hands-On-Large-Language-Models more popular on GitHub? Hands-On-Large-Language-Models has more GitHub stars (29,162 vs 6,965). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM4Decompile and Hands-On-Large-Language-Models open source? Yes - both are open-source projects on GitHub (LLM4Decompile: MIT, Hands-On-Large-Language-Models: Apache-2.0). ### Where can I find alternatives to LLM4Decompile or Hands-On-Large-Language-Models? GraphCanon lists graph-backed alternatives at [LLM4Decompile alternatives](/tools/albertan017-llm4decompile/alternatives) and [Hands-On-Large-Language-Models alternatives](/tools/handsonllm-hands-on-large-language-models/alternatives) ([LLM4Decompile markdown twin](/tools/albertan017-llm4decompile/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/albertan017-llm4decompile-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, LLM4Decompile or Hands-On-Large-Language-Models? LLM4Decompile: 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 LLM4Decompile and Hands-On-Large-Language-Models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM4Decompile trust report](/tools/albertan017-llm4decompile/trust); [Hands-On-Large-Language-Models trust report](/tools/handsonllm-hands-on-large-language-models/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=albertan017-llm4decompile`](/api/graphcanon/graph?tool=albertan017-llm4decompile) - 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/_