A specialist that converts text into dense vector representations, KaLM Embedding Gemma3 12B 2511 is built on a large 12B parameter backbone, giving it substantial capacity to capture nuanced semantic relationships. It handles long contexts up to 131K tokens, making it capable of embedding lengthy documents without truncation. As a pure embedding model, it produces no generated text — its entire output is numerical vectors intended for downstream tasks like search or similarity.