Distilling Transformers into Mamba models
A paper published September 29, 2026 studies transferring knowledge from pretrained Transformers to state space models such as Mamba.
Source: Attention to Mamba: destilação entre arquiteturas (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on September 29, 2026, the paper “Attention to Mamba: cross-architecture distillation” studies how pretrained Transformers can be used to train state space models (SSMs), such as Mamba. It highlights that SSMs can use less memory and provide higher generation throughput than attention-based models, and examines how to transfer knowledge acquired through Transformer training.
A company can assess this approach on representative tasks, comparing response quality, memory use, and throughput with its current models. A practical implementation could include a data preparation and distillation pipeline, reproducible tests, and a dashboard to track quality, latency, and resource use before deciding whether to deploy it.
How the consultancy can help
Wendelmaques can assess the opportunity for your models and infrastructure, define an evaluation with adoption criteria and metrics, and implement and operate the distillation and monitoring pipeline.
Next step
Send a short description of your models, tasks, and infrastructure constraints to receive a scoped proposal.
Let's assess your project
I reply with scope and a proposal.