A compact, instruction-following embedding specialist that converts text into dense vector representations. At 480M parameters, it sits in a lightweight tier — efficient enough to run locally or on modest hardware, while still handling an 8K context window. It follows instructions to shape how embeddings are generated, which can be useful for retrieval and semantic similarity tasks.