PyLate: Flexible Training and Retrieval for Late Interaction Models

By Antoine Chaffin, Raphaël Sourty. 2025

Subfields: Information Retrieval & Search, Representation & Self-Supervised Learning

Citations in the corpus

• ColBERT-Zero: To Pre-train Or Not To Pre-train ColBERT models (2026, unknown): “We use the PyLate [10] library and leverage the implementation of GradCache [11] to scale the per-GPU batch size arbitrarily without VRAM constraints (as standard gradient accumulation is not applicable for contrastive learning).”
• Fantastic (small) Retrievers and How to Train Them: mxbai-edge-colbert-v0 Tech Report (2025, unknown): “All experiments are performed using the PyLate [2] framework.”
• Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings (2025, unknown): “Insurance is composed of a long document with insurance-related statistics for each country of the European Union.”
• DenseOn with the LateOn: Fully Open Dense and Late-Interaction Models for Multilingual, Long-Context, and Code Search (2026, unknown): “For all training phases, including contrastive pre-training and supervised fine-tuning, we use Sentence Transformers (Reimers and Gurevych, 2019) for dense models and PyLate (Chaffin and Sourty, 2025) for late-interaction models.”