--- title: "Awesome-LLMs-ICLR-24 vs LLMmap" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-pasquini-dario-llmmap" tools: ["azminewasi-awesome-llms-iclr-24", "pasquini-dario-llmmap"] --- # Awesome-LLMs-ICLR-24 vs LLMmap *GraphCanon updated Aug 8, 2026* ## 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. [Awesome-LLMs-ICLR-24](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) reports 72 GitHub stars, 5 forks, and 0 open issues, last pushed Apr 4, 2024. [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 [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [LLMmap's repository](https://github.com/pasquini-dario/LLMmap). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [LLMmap](/tools/pasquini-dario-llmmap.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | Provides a ready-to-use pretrained model for open-set inference with PyTorch weights, configuration file, and behavioral templates. | | Stars | 72 | 405 | | Forks | 5 | 46 | | Open issues | 0 | 6 | | Language | - | Python | | Adopt for | 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 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 | MIT | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [LLMmap](/tools/pasquini-dario-llmmap.md) | | --- | --- | --- | | Days since push | 856d | 376d | | Open issues (now) | 0 | 6 | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/pasquini-dario-llmmap/trust.md) | ## Decision facts: Awesome-LLMs-ICLR-24 - **Adopt for:** 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. ## 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-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. ### 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 (405 vs 72) - visibility, not fit. ## 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. ## 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-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 (405 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 (405 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](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [LLMmap alternatives](/tools/pasquini-dario-llmmap/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/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/azminewasi-awesome-llms-iclr-24-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-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](/tools/azminewasi-awesome-llms-iclr-24/trust); [LLMmap trust report](/tools/pasquini-dario-llmmap/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24`](/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24) - 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/_