Fantastic (small) Retrievers and How to Train Them:
  mxbai-edge-colbert-v0 Tech Report
Authors: name: Rikiya Takehi, name: Benjamin Clavié, name: Sean Lee, name: Aamir Shakir
Organization: Mixedbread

In this work, we introduce mxbai-edge-colbert-v0 models, at two different
parameter counts: 17M and 32M. As part of our research, we conduct numerous
experiments to improve retrieval and late-interaction models, which we intend
to distill into smaller models as proof-of-concepts. Our ultimate aim is to
support retrieval at all scales, from large-scale retrieval which lives in the
cloud to models that can run locally, on any device. mxbai-edge-colbert-v0 is a
model that we hope will serve as a solid foundation backbone for all future
experiments, representing the first version of a long series of small
proof-of-concepts. As part of the development of mxbai-edge-colbert-v0, we
conducted multiple ablation studies, of which we report the results. In terms
of downstream performance, mxbai-edge-colbert-v0 is a particularly capable
small model, outperforming ColBERTv2 on common short-text benchmarks (BEIR) and
representing a large step forward in long-context tasks, with unprecedented
efficiency.