Fantastic (small) Retrievers and How to Train Them:
  mxbai-edge-colbert-v0 Tech Report
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.
68f1b4f86e0bef323a68fe21
Rikiya Takehi
claimed_verified
2025-10-20T12:13:18.663Z
661566ddfea780a4f43528ff
https://cdn-avatars.huggingface.co/v1/production/uploads/661566ddfea780a4f43528ff/7gzbMoG4lU5R1oC7jQYik.jpeg
Rikiya Takehi
user
RikiyaT
68f1b4f86e0bef323a68fe22
Benjamin Clavié
claimed_verified
2025-10-17T04:10:11.390Z
5ff60d4352c26e9bc240badd
https://cdn-avatars.huggingface.co/v1/production/uploads/5ff60d4352c26e9bc240badd/HzoknJibrSasc1ZzU71XA.png
Benjamin Clavié
user
bclavie
68f1b4f86e0bef323a68fe23
Sean Lee
68f1b4f86e0bef323a68fe24
Aamir Shakir
Fantastic (small) Retrievers and How to Train Them:
  mxbai-edge-colbert-v0 Tech Report
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.
mxbai-edge-colbert-v0 models, with 17M and 32M parameters, demonstrate superior retrieval performance on short-text and long-context benchmarks compared to ColBERTv2.
mxbai-edge-colbert-v0 retrieval late-interaction models distillation ablation studies BEIR ColBERTv2 long-context tasks