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ModernBERT

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Laya the open source version of Jev

Convai Innovations released Laya, an open-source Apache 2.0 family of non-autoregressive decision models claiming 6-8x faster inference than TypeSafe AI's Jev.

Convai Innovations launched Laya, a family of non-autoregressive 'System 1' decision models that output calibrated probabilities over schemas instead of generating text. It ships three Apache 2.0 checkpoints on Hugging Face: laya (421M, ModernBERT-large), laya-multilingual (322M, mmBERT-base, 100+ languages), and laya-typed-decisions (421M, 0.766 accuracy on security alerts), running in 32.8 ms on a single GPU (7.2 ms batched). The release positions Laya as an open alternative to TypeSafe AI's Jev, a commercial decision API priced at $0.042 per million input tokens with ~150 ms responses. The author cites prior arXiv papers (2503.23303, 2510.01237) and a 51-language MASSIVE benchmark sweep showing English-only encoders fail on non-Latin scripts (Khmer: 0.000 accuracy at 0.952 confidence).

Linkup Research Releases SPARSEUP: A 149M-Parameter Open-Source Sparse Embedding Model

Linkup Research released SPARSEUP, a 149M-parameter open-source sparse embedding model under Apache 2.0, scoring 56.4 nDCG@10 on BEIR-13.

Linkup Research released SPARSEUP, an open sparse embedding model built on a 149M-parameter ModernBERT backbone with weights available on Hugging Face under Apache 2.0. It was fine-tuned from LightOn's LateOn checkpoint using contrastive learning with hard negatives, adding a log(1+ReLU(x-15)) logit shift, per-token top-12 expansion, and byte-BPE case folding to control sparsity. Linkup reports 56.4 average nDCG@10 on BEIR-13, ahead of opensearch-neural-sparse (54.6) and splade-v3 (51.7), though it trails LightOn's DenseOn (57.9) and LateOn (58.9) under identical backbone and data. With a Seismic inverted index it achieves over 97% recall versus exact search at roughly 380 microseconds per query on MS MARCO.

MarkTechPost · 19h agoModel release1