--- title: "LLM4Decompile vs Awesome-LLM-Compression" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/albertan017-llm4decompile-vs-huangowen-awesome-llm-compression" tools: ["albertan017-llm4decompile", "huangowen-awesome-llm-compression"] --- # LLM4Decompile vs Awesome-LLM-Compression *GraphCanon updated Aug 17, 2026* ## Verdict Pick LLM4Decompile if lLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code; pick Awesome-LLM-Compression if awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. [LLM4Decompile](https://aclanthology.org/2024.emnlp-main.203) reports 7.0k GitHub stars, 546 forks, and 46 open issues, last pushed Feb 12, 2026. [Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) has 1.9k stars, 129 forks, and 1 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [LLM4Decompile's repository](https://github.com/albertan017/LLM4Decompile) and [Awesome-LLM-Compression's repository](https://github.com/HuangOwen/Awesome-LLM-Compression). | | [LLM4Decompile](/tools/albertan017-llm4decompile.md) | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | | --- | --- | --- | | Tagline | Decompiling Binary Code with Large Language Models | Awesome LLM compression research papers and tools to accelerate LLM training and inference. | | Stars | 6,965 | 1,859 | | Forks | 546 | 129 | | Open issues | 46 | 1 | | Language | Python | - | | Adopt for | LLM4Decompile uses large language models to reverse engineer binary code into assembly instructions and potentially source code. | Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License | | Categories | LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLM4Decompile](/tools/albertan017-llm4decompile.md) | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 186d | 37d | | Open issues (now) | 46 | 1 | | Stars delta | +205 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/albertan017-llm4decompile/trust.md) | [trust report](/tools/huangowen-awesome-llm-compression/trust.md) | ## 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: Awesome-LLM-Compression - **Requirements:** The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable. - **Adopt for:** Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. - **License detail:** MIT License ## Choose when ### Choose LLM4Decompile if… - 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.. - Tags unique to LLM4Decompile: binary, decompile, large language models, 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 Awesome-LLM-Compression if… - Requirements: The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.. - Tags unique to Awesome-LLM-Compression: compression, efficiency, research papers, training acceleration. - Also covers Inference & Serving. - When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ## 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 Awesome-LLM-Compression - Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information. - If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects. ## Common questions ### What is the difference between LLM4Decompile and Awesome-LLM-Compression? LLM4Decompile: Decompiling Binary Code with Large Language Models. Awesome-LLM-Compression: Awesome LLM compression research papers and tools to accelerate LLM training and inference.. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLM4Decompile over Awesome-LLM-Compression? Choose LLM4Decompile over Awesome-LLM-Compression when 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.; Tags unique to LLM4Decompile: binary, decompile, large language models, 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 Awesome-LLM-Compression over LLM4Decompile? Choose Awesome-LLM-Compression over LLM4Decompile when Requirements: The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.; Tags unique to Awesome-LLM-Compression: compression, efficiency, research papers, training acceleration; Also covers Inference & Serving; When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### 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 Awesome-LLM-Compression? Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information. If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects. ### Is LLM4Decompile or Awesome-LLM-Compression more popular on GitHub? LLM4Decompile has more GitHub stars (6,965 vs 1,859). Stars measure visibility, not whether either tool fits your constraints. ### Are LLM4Decompile and Awesome-LLM-Compression open source? Yes - both are open-source projects on GitHub (LLM4Decompile: MIT, Awesome-LLM-Compression: MIT). ### Where can I find alternatives to LLM4Decompile or Awesome-LLM-Compression? GraphCanon lists graph-backed alternatives at [LLM4Decompile alternatives](/tools/albertan017-llm4decompile/alternatives) and [Awesome-LLM-Compression alternatives](/tools/huangowen-awesome-llm-compression/alternatives) ([LLM4Decompile markdown twin](/tools/albertan017-llm4decompile/alternatives.md), [Awesome-LLM-Compression markdown twin](/tools/huangowen-awesome-llm-compression/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-huangowen-awesome-llm-compression.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLM4Decompile or Awesome-LLM-Compression? LLM4Decompile: Slowing. Awesome-LLM-Compression: Steady. 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 Awesome-LLM-Compression? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLM4Decompile trust report](/tools/albertan017-llm4decompile/trust); [Awesome-LLM-Compression trust report](/tools/huangowen-awesome-llm-compression/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/_