Language-specific retrieval models can match much larger models through targeted fine-tuning with distillation, and open-sourcing training data and procedures enables reproducible progress in underserved languages like Persian.
Yalda Embedding is a 596M-parameter Persian retrieval model fine-tuned from Qwen3-Embedding using over 1 million query-document pairs. It outperforms comparable 0.6B models on Persian retrieval tasks and approaches 4B model performance, with gains from knowledge distillation and contrastive learning. The authors release all code, weights, and training data to support Persian NLP research.