Comparison
LLMSys-PaperList vs LLMmap
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.
Markdown twin · LLMSys-PaperList alternatives · LLMmap alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | LLMSys-PaperList | LLMmap |
|---|---|---|
| Maintenance | Active (12d since push) As of 2w · github_public_v1 | Dormant (376d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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.
Stars
- LLMSys-PaperList
- 2.2k
- LLMmap
- 405
Forks
- LLMSys-PaperList
- 120
- LLMmap
- 46
Open issues
- LLMSys-PaperList
- 1
- LLMmap
- 6
Language
- LLMSys-PaperList
- Python
- LLMmap
- Python
Adopt for
- LLMSys-PaperList
- LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems.
- LLMmap
- 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
- LLMSys-PaperList
- -
- LLMmap
- -
Runtime
- LLMSys-PaperList
- -
- LLMmap
- -
License
- LLMSys-PaperList
- (unknown)
- LLMmap
- MIT
Last pushed
- LLMSys-PaperList
- Jul 25, 2026
- LLMmap
- Jul 24, 2025
Categories
- LLMSys-PaperList
- Inference & Serving, LLM Frameworks, Model Training
- LLMmap
- Inference & Serving, Model Training
Trust and health
Maintenance
- LLMSys-PaperList
- Active (82%)
- LLMmap
- Dormant (18%)
Days since push
- LLMSys-PaperList
- 12d
- LLMmap
- 376d
Open issues (now)
- LLMSys-PaperList
- 1
- LLMmap
- 6
OSV dependency advisories
- LLMSys-PaperList
- No lockfile (source not queried)
- LLMmap
- Published findings
Full report
- LLMSys-PaperList
- Trust report
- LLMmap
- Trust report
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.
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
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (AmberLJC/LLMSys-PaperList) · observed Aug 6, 2026
- GitHub forks (AmberLJC/LLMSys-PaperList) · observed Aug 6, 2026
- Last push (AmberLJC/LLMSys-PaperList) · observed Jul 25, 2026
- License file (unknown) · observed Aug 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (pasquini-dario/LLMmap) · observed Aug 5, 2026
- GitHub forks (pasquini-dario/LLMmap) · observed Aug 5, 2026
- Last push (pasquini-dario/LLMmap) · observed Jul 24, 2025
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: LLMSys-PaperList 2.2k · LLMmap 405 (synced Aug 6, 2026).
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 and LLMmap alternatives (LLMSys-PaperList markdown twin, LLMmap markdown twin), 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 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; LLMmap trust report.