--- title: "LLMSys-PaperList vs LLMmap" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/amberljc-llmsys-paperlist-vs-pasquini-dario-llmmap" tools: ["amberljc-llmsys-paperlist", "pasquini-dario-llmmap"] --- # LLMSys-PaperList vs LLMmap *GraphCanon updated Aug 6, 2026* ## Verdict Pick LLMSys-PaperList if lLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems; 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 52 different LLMs. [LLMSys-PaperList](https://github.com/AmberLJC/LLMSys-PaperList) reports 2.2k GitHub stars, 120 forks, and 1 open issues, last pushed Jul 25, 2026. [LLMmap](https://github.com/pasquini-dario/LLMmap) has 405 stars, 46 forks, and 6 open issues, last pushed Jul 24, 2025. Figures are from public GitHub metadata via [LLMSys-PaperList's repository](https://github.com/AmberLJC/LLMSys-PaperList) and [LLMmap's repository](https://github.com/pasquini-dario/LLMmap). | | [LLMSys-PaperList](/tools/amberljc-llmsys-paperlist.md) | [LLMmap](/tools/pasquini-dario-llmmap.md) | | --- | --- | --- | | Tagline | Curated list of academic papers related to Large Language Model systems | Provides a ready-to-use pretrained model for open-set inference with PyTorch weights, configuration file, and behavioral templates. | | Stars | 2,220 | 405 | | Forks | 120 | 46 | | Open issues | 1 | 6 | | Language | Python | Python | | Adopt for | LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems. | 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 | (unknown) | MIT | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMSys-PaperList](/tools/amberljc-llmsys-paperlist.md) | [LLMmap](/tools/pasquini-dario-llmmap.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 12d | 376d | | Open issues (now) | 1 | 6 | | Full report | [trust report](/tools/amberljc-llmsys-paperlist/trust.md) | [trust report](/tools/pasquini-dario-llmmap/trust.md) | ## Decision facts: LLMSys-PaperList - **Hosting:** unknown - (repository does not specify hosting environment) - **Adopt for:** LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems. - **License detail:** (unknown) ## 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 LLMSys-PaperList if… - (repository does not specify hosting environment) - Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers. - Also covers LLM Frameworks. - - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems. ### Choose LLMmap if… - Tags unique to LLMmap: llms, open-set inference, pretrained-models, python. - 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 LLMSys-PaperList - - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - - When your primary need is documentation or code examples rather than academic papers and project insights. - - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ ## 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 LLMSys-PaperList and LLMmap? LLMSys-PaperList: Curated list of academic papers related to Large Language Model systems. 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 LLMSys-PaperList over LLMmap? Choose LLMSys-PaperList over LLMmap when (repository does not specify hosting environment); Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers; Also covers LLM Frameworks; - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems. ### When should I choose LLMmap over LLMSys-PaperList? Choose LLMmap over LLMSys-PaperList when Tags unique to LLMmap: llms, open-set inference, pretrained-models, python; 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 LLMSys-PaperList? - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - When your primary need is documentation or code examples rather than academic papers and project insights. - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ ### 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 LLMSys-PaperList or LLMmap more popular on GitHub? LLMSys-PaperList has more GitHub stars (2,220 vs 405). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSys-PaperList and LLMmap open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSys-PaperList or LLMmap? GraphCanon lists graph-backed alternatives at [LLMSys-PaperList alternatives](/tools/amberljc-llmsys-paperlist/alternatives) and [LLMmap alternatives](/tools/pasquini-dario-llmmap/alternatives) ([LLMSys-PaperList markdown twin](/tools/amberljc-llmsys-paperlist/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/amberljc-llmsys-paperlist-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, LLMSys-PaperList or LLMmap? LLMSys-PaperList: Active. LLMmap: Dormant. 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 LLMSys-PaperList and LLMmap? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSys-PaperList trust report](/tools/amberljc-llmsys-paperlist/trust); [LLMmap trust report](/tools/pasquini-dario-llmmap/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=amberljc-llmsys-paperlist`](/api/graphcanon/graph?tool=amberljc-llmsys-paperlist) - 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/_