1 citations · 1 across the 3 of their papers we have counts for
8 papers
Apriel-H1: Towards Efficient Enterprise Reasoning Models
Oleksiy Ostapenko, Luke Kumar, Raymond Li +10
Large Language Models (LLMs) achieve remarkable reasoning capabilities through transformer architectures with attention mechanisms. However, transformers suffer from quadratic time…
ColMate: Contrastive Late Interaction and Masked Text for Multimodal Document Retrieval
Ahmed Masry, Megh Thakkar, Patrice Bechard +9
Retrieval-augmented generation has proven practical when models require specialized knowledge or access to the latest data. However, existing methods for multimodal document retrie…
Apriel-1.5-15b-Thinker
Shruthan Radhakrishna, Aman Tiwari, Aanjaneya Shukla +21
We present Apriel-1.5-15B-Thinker, a 15-billion parameter open-weights multimodal reasoning model that achieves frontier-level performance through training design rather than sheer…
Apriel-Nemotron-15B-Thinker
Shruthan Radhakrishna, Soham Parikh, Gopal Sarda +32
While large language models (LLMs) have achieved remarkable reasoning capabilities across domains like code, math and other enterprise tasks, their significant memory and computati…
Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training
Oleksiy Ostapenko, Charles Guille-Escuret, Luke Kumar +7
We introduce a framework for optimizing domain-specific dataset construction in foundation model training. Specifically, we seek a cost-efficient way to estimate the quality of dat…
Unifying Autoregressive and Diffusion-Based Sequence Generation
Nima Fathi, Torsten Scholak, Pierre-André Noël
We present significant extensions to diffusion-based sequence generation models, blurring the line with autoregressive language models. We introduce hyperschedules, which assign di…