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ZIJIA CHEN

3 accepted papers

2025

Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning

NeurIPS 2025poster

Hybrid language models that combine Attention and State Space Models (SSMs) have been shown to achieve state-of-the-art accuracy and runtime performance. Recent work has also demonstrated that applying pruning and distillation to Attention-only models yields smaller, more accurate models at a fracti…

Cited by 0SourceScholar
2025

Hymba: A Hybrid-head Architecture for Small Language Models

ICLR 2025spotlight

We propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates attention mechanisms and state space models (SSMs) within the same layer, offering parallel and complementary processing of the same inputs. In this hybrid-head module, attention heads p…

Cited by 12SourcePDFScholar
2025

Nemotron-CLIMB: Clustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training

NeurIPS 2025spotlight

Pre-training datasets are typically collected from web content and lack inherent domain divisions. For instance, widely used datasets like Common Crawl do not include explicit domain labels, while manually curating labeled datasets such as The Pile is labor-intensive. Consequently, identifying an op…

Cited by 0SourceScholar