Anna Models contributed to the embeddings-benchmark/mteb repository by integrating support for the LGAI-Embedding-Preview model, expanding the benchmark’s evaluation coverage. She implemented this feature using Python, focusing on model integration and benchmark contribution. Her work involved adding the new model’s metadata, training datasets, and configuration, ensuring seamless compatibility with existing benchmarking pipelines. By registering the model in the MTEB model overview, Anna enabled immediate and standardized evaluation alongside other embedding models. This integration streamlines the benchmarking workflow, reduces setup time for future models, and supports data-driven decisions on model adoption. The work demonstrates depth in benchmarking infrastructure and model onboarding.