Hugging Face has released version 6.0 of Sentence Transformers with a new MultiVectorEncoder model type, enabling practitioners to train and finetune ColBERT-style late interaction retrieval models. The update introduces a complete training framework including datasets, loss functions, training arguments, evaluators, and trainer classes, designed to make multi-vector model development accessible on consumer GPUs. Unlike dense embedding models that compress text into a single vector, multi-vector models preserve one vector per token and match queries to documents using a MaxSim operator. This token-level granularity captures fine-grained domain signals that single-vector approaches average away. The approach is particularly effective for domain-specific tasks where vocabulary, query patterns, and relevance criteria differ from web search data. Hugging Face demonstrated the framework's practical impact with a medical retrieval model (mLateOn-medical) trained in 14.5 hours on a single RTX 3090, which outperformed general-purpose dense, sparse, lexical, and multi-vector retrievers. The library also addresses a critical limitation of existing models: handling longer documents. Many production models truncate inputs at 256-512 tokens, discarding the majority of longer documents; the new training approach allows practitioners to configure appropriate document lengths for their specific data.