Sentence Transformers v6.0 introduces MultiVectorEncoder, enabling developers to train and finetune ColBERT-style multi-vector embedding models for domain-specific retrieval tasks. Unlike traditional dense embedding models that compress entire texts into single vectors, multi-vector models preserve token-level representations, allowing more fine-grained semantic matching. This approach significantly improves retrieval performance for specialized domains like medical literature, legal discovery, and code search. The new training framework makes building custom retrieval models accessible to practitioners with limited resources. A finetuned medical retrieval model trained on a single RTX 3090 GPU in 14.5 hours outperformed all tested general-purpose retrievers, including dense, sparse, and lexical models. The approach also addresses a common limitation of existing models: their truncation of long documents. Many production models discard content beyond 256-512 tokens, costing up to 0.24 NDCG@10 on longer passages. With custom training, developers can configure optimal document lengths for their specific data. The release includes complete training infrastructure—models, datasets, loss functions, evaluators, and trainer classes—all available through standard pip installation. The companion documentation provides both training examples and guidance on indexing in vector databases, making multi-vector retrieval practical for production retrieval-augmented generation systems.