Comparison
Awesome-LLMs-ICLR-24 vs LLMmap
Verdict
Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; 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 · Awesome-LLMs-ICLR-24 alternatives · LLMmap alternatives
GraphCanon updated Oct 5, 2026
6views this month
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Trust & integrity
| Signal | Awesome-LLMs-ICLR-24 | LLMmap |
|---|---|---|
| Maintenance | Dormant (887d since push) As of Sep 9, 2026 · github_public_v1 | Dormant (437d since push) As of Oct 5, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 9, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 5, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- Awesome-LLMs-ICLR-24
- Compilation of LLM papers from ICLR 2024
- LLMmap
- Provides a ready-to-use pretrained model for open-set inference with PyTorch weights, configuration file, and behavioral templates.
Stars
- Awesome-LLMs-ICLR-24
- 72
- LLMmap
- 469
Forks
- Awesome-LLMs-ICLR-24
- 5
- LLMmap
- 51
Open issues
- Awesome-LLMs-ICLR-24
- 0
- LLMmap
- 6
Language
- Awesome-LLMs-ICLR-24
- -
- LLMmap
- Python
Adopt for
- Awesome-LLMs-ICLR-24
- Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024.
- 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
- Awesome-LLMs-ICLR-24
- -
- LLMmap
- -
Runtime
- Awesome-LLMs-ICLR-24
- -
- LLMmap
- -
License
- Awesome-LLMs-ICLR-24
- MIT
- LLMmap
- MIT
Last pushed
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
- LLMmap
- Jul 24, 2025
Categories
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- LLMmap
- Inference & Serving, Model Training
Trust and health
Days since push
- Awesome-LLMs-ICLR-24
- 887d
- LLMmap
- 437d
Open issues (now)
- Awesome-LLMs-ICLR-24
- 0
- LLMmap
- 6
Stars delta
- Awesome-LLMs-ICLR-24
- 0 (30d)
- LLMmap
- +25 (30d)
OSV dependency advisories
- Awesome-LLMs-ICLR-24
- No lockfile (source not queried)
- LLMmap
- Published findings
Full report
- Awesome-LLMs-ICLR-24
- Trust report
- LLMmap
- Trust report
Choose Awesome-LLMs-ICLR-24 if…
- Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework.
- Also covers Developer Tools, Evaluation & Observability, LLM Frameworks.
- If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
When NOT to use Awesome-LLMs-ICLR-24
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024.
- For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
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.
- More GitHub stars (469 vs 72) - visibility, not fit.
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 (azminewasi/Awesome-LLMs-ICLR-24) · observed Sep 20, 2026
- GitHub forks (azminewasi/Awesome-LLMs-ICLR-24) · observed Sep 20, 2026
- Last push (azminewasi/Awesome-LLMs-ICLR-24) · observed Apr 4, 2024
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (pasquini-dario/LLMmap) · observed Oct 5, 2026
- GitHub forks (pasquini-dario/LLMmap) · observed Oct 5, 2026
- Last push (pasquini-dario/LLMmap) · observed Jul 24, 2025
- License file (MIT) · observed Oct 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMs-ICLR-24 72 · LLMmap 469 (synced Sep 20, 2026).
Common questions
- What is the difference between Awesome-LLMs-ICLR-24 and LLMmap?
- Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. 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-LLMs-ICLR-24 over LLMmap?
- Choose Awesome-LLMs-ICLR-24 over LLMmap when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Evaluation & Observability, LLM Frameworks; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights.
- When should I choose LLMmap over Awesome-LLMs-ICLR-24?
- Choose LLMmap over Awesome-LLMs-ICLR-24 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; More GitHub stars (469 vs 72) - visibility, not fit.
- When should I avoid Awesome-LLMs-ICLR-24?
- If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis.
- 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-LLMs-ICLR-24 or LLMmap more popular on GitHub?
- LLMmap has more GitHub stars (469 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-LLMs-ICLR-24 and LLMmap open source?
- Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, LLMmap: MIT).
- Where can I find alternatives to Awesome-LLMs-ICLR-24 or LLMmap?
- GraphCanon lists graph-backed alternatives at Awesome-LLMs-ICLR-24 alternatives and LLMmap alternatives (Awesome-LLMs-ICLR-24 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, Awesome-LLMs-ICLR-24 or LLMmap?
- Awesome-LLMs-ICLR-24: Dormant. 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 Awesome-LLMs-ICLR-24 and LLMmap?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-LLMs-ICLR-24 trust report; LLMmap trust report.