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Ryan Teknium, Roger Jin, Jai Suphavadeeprasit, Dakota Mahan, Jeffrey Quesnelle, Joe Li, Chen Guang, Shannon Sands, Karan Malhotra
Outline
Hermes 4 is a family of hybrid inference models that combine structured multi-turn inference with extensive instruction-following capabilities. We describe the challenges encountered during data curation, synthesis, training, and evaluation, and the solutions used to address them at scale. We perform a comprehensive evaluation across mathematical reasoning, coding, knowledge, comprehension, and alignment benchmarks, reporting quantitative performance and qualitative behavioral analysis. All model weights are publicly shared at https://huggingface.co/collections/NousResearch/hermes-4-collection-68a731bfd452e20816725728 .
Takeaways, Limitations
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Takeaways: Demonstrates the effectiveness of a hybrid model that combines various inference capabilities. Provides practical solutions for building large-scale datasets and training models. Promotes research sharing and advancement through open model weight disclosure.
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Limitations: The paper lacks specific Limitations or future research directions. A detailed analysis of the degree of performance improvement and limitations across various benchmarks is needed. The lack of a detailed description of the data curation and synthesis process may reduce reproducibility.
