--- title: "Awesome-LLM-Compression vs LLMmap" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huangowen-awesome-llm-compression-vs-pasquini-dario-llmmap" tools: ["huangowen-awesome-llm-compression", "pasquini-dario-llmmap"] --- # Awesome-LLM-Compression vs LLMmap *GraphCanon updated Jul 12, 2026* ## Verdict 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; pick LLMmap if lLMmap is a Python-based tool for quick inference using pretrained models without needing additional training. It includes PyTorch weights, configuration files, and behavioral templates tailored to. [Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) reports 1.8k GitHub stars, 128 forks, and 0 open issues, last pushed Jun 30, 2026. [LLMmap](https://github.com/pasquini-dario/LLMmap) has 371 stars, 42 forks, and 6 open issues, last pushed Jul 24, 2025. Figures are from public GitHub metadata via [Awesome-LLM-Compression's repository](https://github.com/HuangOwen/Awesome-LLM-Compression) and [LLMmap's repository](https://github.com/pasquini-dario/LLMmap). | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [LLMmap](/tools/pasquini-dario-llmmap.md) | | --- | --- | --- | | Tagline | Awesome LLM compression research papers and tools to accelerate LLM training and inference. | Provides a ready-to-use pretrained model for open-set inference with PyTorch weights, configuration file, and behavioral templates. | | Stars | 1,848 | 371 | | Forks | 128 | 42 | | Open issues | 0 | 6 | | Language | - | Python | | 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. | LLMmap is a Python-based tool for quick inference using pretrained models without needing additional training. It includes PyTorch weights, configuration files, and behavioral templates tailored to 52 different LLMs. | | Persona | - | - | | Runtime | - | - | | License | MIT License | MIT | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [LLMmap](/tools/pasquini-dario-llmmap.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 10d | 352d | | Open issues (now) | 0 | 6 | | Security scan | No lockfile | 32 low (32 low) | | Full report | [trust report](/tools/huangowen-awesome-llm-compression/trust.md) | [trust report](/tools/pasquini-dario-llmmap/trust.md) | ## 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 ## Decision facts: LLMmap - **Adopt for:** LLMmap is a Python-based tool for quick inference using pretrained models without needing additional training. It includes PyTorch weights, configuration files, and behavioral templates tailored to 52 different LLMs. ## Choose when ### 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 LLM Frameworks. - When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### Choose LLMmap if… - Tags unique to LLMmap: llms, open-set inference, pretrained models, python. - Also covers Model Training. - When you need immediate model deployment and don't want or can’t afford the time to train a custom 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. ## When NOT to use LLMmap - If your application requires fine-tuning on specific datasets as LLMmap offers only generic pretrained models without out-of-the-box support for further training. - In scenarios needing advanced customization beyond the provided behavioral templates, since LLMmap’s framework might not accommodate extensive model modifications. ## Common questions ### What is the difference between Awesome-LLM-Compression and LLMmap? Awesome-LLM-Compression: Awesome LLM compression research papers and tools to accelerate LLM training and inference.. LLMmap: Provides a ready-to-use pretrained model for open-set inference with PyTorch weights, configuration file, and behavioral templates.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Compression over LLMmap? Choose Awesome-LLM-Compression over LLMmap 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 LLM Frameworks; When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### When should I choose LLMmap over Awesome-LLM-Compression? Choose LLMmap over Awesome-LLM-Compression when Tags unique to LLMmap: llms, open-set inference, pretrained models, python; Also covers Model Training; When you need immediate model deployment and don't want or can’t afford the time to train a custom 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. ### When should I avoid LLMmap? If your application requires fine-tuning on specific datasets as LLMmap offers only generic pretrained models without out-of-the-box support for further training. In scenarios needing advanced customization beyond the provided behavioral templates, since LLMmap’s framework might not accommodate extensive model modifications. ### Is Awesome-LLM-Compression or LLMmap more popular on GitHub? Awesome-LLM-Compression has more GitHub stars (1,848 vs 371). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Compression and LLMmap open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Compression: MIT, LLMmap: MIT). ### Where can I find alternatives to Awesome-LLM-Compression or LLMmap? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Compression alternatives](/tools/huangowen-awesome-llm-compression/alternatives) and [LLMmap alternatives](/tools/pasquini-dario-llmmap/alternatives) ([Awesome-LLM-Compression markdown twin](/tools/huangowen-awesome-llm-compression/alternatives.md), [LLMmap markdown twin](/tools/pasquini-dario-llmmap/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/huangowen-awesome-llm-compression-vs-pasquini-dario-llmmap.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLM-Compression or LLMmap? Awesome-LLM-Compression: Active. LLMmap: 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 Awesome-LLM-Compression and LLMmap? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Compression trust report](/tools/huangowen-awesome-llm-compression/trust); [LLMmap trust report](/tools/pasquini-dario-llmmap/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huangowen-awesome-llm-compression`](/api/graphcanon/graph?tool=huangowen-awesome-llm-compression) - 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/_