---
license: apache-2.0
tags:
- mamba
- state-space-model
- sequence-modeling
- selective-scan
- gradio
---

# MicroMamba

MicroMamba is a small-compute test of input-dependent state-space dynamics. Each
sequence contains distracting symbols, a few marked symbols, and a final query asking
for one marked item by ordinal position. Solving the task requires selective storage
and retrieval rather than ordinary next-token statistics.

The model uses a compact Mamba-inspired block with:

- a causal depthwise convolution;
- learned stable diagonal state dynamics;
- input-dependent discretization, input, and readout terms;
- gated residual output.

The benchmark retains two controls: a state-space model whose dynamics do not depend
on the current input and a GRU with comparable scale. This is a pedagogical
Mamba-inspired implementation, not a bit-exact reproduction of the official Mamba
kernel or its large-scale language-model results.

## Verified results

All variants trained on the same 12,000 length-48 sequences and were evaluated on
4,000 independently generated sequences at each length.

| Variant | Parameters | Length 48 | Length 96 zero-shot |
| --- | ---: | ---: | ---: |
| Selective SSM | 4,594 | 87.85% | 87.23% |
| Fixed-dynamics SSM | 3,314 | 43.23% | 31.05% |
| GRU control | 7,146 | 69.38% | 70.00% |

On this controlled task, input-dependent state dynamics improved in-distribution
accuracy by 44.63 points over fixed dynamics and 18.48 points over the larger GRU.
The result is specific to this synthetic selective-memory benchmark.

## Reproduce

```powershell
uv run python projects/micro-mamba/train.py
```