# MultiModalMamba: Fusing Vision Transformer and Mamba for Multi-Modal PyTorch Models

> MultiModalMamba is a PyTorch library that combines a Vision Transformer encoder with a Mamba state space model to process text, image, audio, and video inputs in a single model. It targets ML researchers who want to experiment with SSM-based multi-modal architectures without writing the integration code from scratch.

**kyegomez/MultiModalMamba** — A novel implementation of fusing ViT with Mamba into a fast, agile, and high performance Multi-Modal Model. Powered by Zeta, the simplest AI framework ever.

- Repository: https://github.com/kyegomez/MultiModalMamba
- Website: https://discord.gg/GYbXvDGevY
- Stars: 475 · Forks: 28
- Language: Python
- License: MIT
- Published: 2026-09-10 · Updated: 2026-09-10 · Language: en
- Canonical page: https://hysenlabs.com/projects/kyegomez-multimodalmamba

## What MultiModalMamba Is and the Problem It Addresses

Processing different data types, text alongside images, audio, or video, typically requires either multiple separate models or a complex fusion architecture. Vision Transformers handle images well but are not designed for sequential text processing at scale. Mamba, a state space model architecture, processes sequences efficiently without the quadratic attention cost of transformers but was originally designed for text.

MultiModalMamba (MMM) addresses this by integrating a Vision Transformer encoder with a Mamba backbone. The ViT encoder processes image patches into embeddings, and those embeddings are passed into the Mamba layers alongside token embeddings from text. The README describes support for audio and video tensors as well, handled through reshape operations before fusion.

The library is built on Zeta, described in the README as a minimalist AI framework. It targets ML researchers and engineers who want to experiment with SSM-based multi-modal models in PyTorch without building the ViT-Mamba integration themselves.

## Core Components: MultiModalMambaBlock and MultiModalMamba

The library exposes two primary classes from the mm_mamba module.

MultiModalMambaBlock is the lower-level building block. It takes a text sequence tensor of shape (batch_size, sequence_length, feature_dim) and an image tensor of shape (batch_size, channels, height, width), and applies Mamba layers with a configurable depth, dropout rate, and number of attention heads. This block is intended to be stacked or embedded in a larger architecture.

MultiModalMamba is the higher-level, ready-to-train model. It adds configurable parameters for image processing: image_size, patch_size, encoder_dim, encoder_depth, encoder_heads, and fusion_method. It also supports a return_embeddings flag that returns intermediate representations instead of final predictions, useful for transfer learning or feature extraction. The README presents an example that passes text token tensors, image tensors, audio tensors, and video tensors together to the same model instance.

## Installing mmm-zeta and Running the Building Block

The package installs as mmm-zeta on PyPI:

```bash
pip3 install mmm-zeta
```

The requirements.txt pins torch to 2.1.2 and zetascale to 1.4.0. Python 3.6 or newer is listed in pyproject.toml.

The README shows a minimal usage example for MultiModalMambaBlock:

```python
import torch
from torch import nn
from mm_mamba import MultiModalMambaBlock

x = torch.randn(1, 16, 64)
y = torch.randn(1, 3, 64, 64)

model = MultiModalMambaBlock(
    dim=64,
    depth=5,
    dropout=0.1,
    heads=4,
)
```

The x tensor represents a text or token sequence and y represents an image. The model processes both through the Mamba layers with the ViT-encoded image embeddings fused into the sequence.

For the full MultiModalMamba model, the README example includes an audio tensor of shape (1, 224) and a video tensor of shape (1, 3, 16, 224, 224), showing the expected input format for each modality.

## Architecture: ViT Encoder, Mamba Layers, and Fusion

The fusion approach replaces the separate visual transformer encoder used in attention-based multi-modal models. Instead of passing image features through cross-attention layers, MultiModalMamba encodes image patches with the ViT encoder into a fixed-size representation and then feeds those embeddings directly into the Mamba state space layers alongside token embeddings. The README does not document a separate audio encoder; audio is described as being processed through reshapes before fusion.

The Mamba layers provide the sequential processing backbone. State space models like Mamba process sequences in linear time relative to sequence length, as opposed to the quadratic cost of self-attention. This property makes them appealing for long sequences, though the practical speed advantage depends heavily on sequence length and hardware.

The fusion_method parameter in MultiModalMamba suggests multiple fusion strategies are available, though the README does not list or compare them. Researchers who need to compare fusion approaches would need to read the source code in mm_mamba/ directly.

## Limitations and How MultiModalMamba Compares to LLaVA

The library ships with no pre-trained weights and no benchmark results in the repository. The README contains claims about being fast, agile, and high performance, but these are assertions without measurements. A researcher choosing this library over alternatives should expect to train from scratch and evaluate on their own tasks.

The pyproject.toml lists the supported Python version as 3.6 or newer, but the dependency on torch 2.1.2 effectively requires a newer Python in practice, since modern PyTorch versions have dropped support for Python 3.6 and 3.7. Testing the install on your target Python version is necessary before committing to this library as a baseline. The pre-commit configuration in .pre-commit-config.yaml and the ruff linter settings in pyproject.toml indicate that the codebase enforces a code style standard, which is useful for contributors but does not affect runtime behaviour.

The scripts/ directory and tests/ directory are present but the README does not describe their contents or how to run the test suite. Contributors would need to read those directories directly to understand the testing setup and any available utility scripts.

The version pinning in requirements.txt is strict: torch 2.1.2 and zetascale 1.4.0. Projects with dependency constraints from other packages may find these pins conflict with their environment. The zetascale package is not part of the standard PyTorch ecosystem, which adds a dependency on a third-party project whose release cadence is outside this repository's control.

For comparison, LLaVA is a widely known open-source multi-modal model that uses a language model backbone connected to a CLIP-based vision encoder through an MLP projection layer. LLaVA is a full trained model with published weights, available in several sizes, and evaluated on standard benchmarks. MultiModalMamba differs in using Mamba instead of a transformer backbone and in supporting audio and video inputs alongside images. It is a research architecture toolkit rather than a trained deployable model.

The repository is MIT licensed. The last push was on 2026-09-25.

## Conclusion

MultiModalMamba is a reasonable starting point for researchers who want to prototype a Mamba-based multi-modal model that handles text alongside images, audio, and video tensors, without writing the ViT-to-Mamba integration themselves. It is not a trained model or a production library. The README contains promotional claims that are not backed by benchmarks in the repository. Before building on this, verify that torch 2.1.2 and zetascale 1.4.0 install cleanly in your environment, since the requirements.txt pins specific versions that may conflict with other packages.

## FAQ

### What input types does MultiModalMamba accept?

The README shows examples with text token tensors, image tensors, audio tensors, and video tensors passed to the same MultiModalMamba model instance. Image input uses shape (batch, channels, height, width), audio uses a 2D tensor, and video uses a 5D tensor (batch, channels, frames, height, width).

### What type of model is Mamba?

Mamba is a state space model (SSM) that processes sequences in linear time relative to sequence length, in contrast to transformers where self-attention costs grow quadratically. MultiModalMamba uses Mamba layers as the backbone for sequential processing.

### What is the mmm-zeta package?

mmm-zeta is the PyPI package name for MultiModalMamba. It installs the mm_mamba Python module containing the MultiModalMambaBlock and MultiModalMamba classes, built on the Zeta framework.

## Sources

- [Issues](https://github.com/kyegomez/MultiModalMamba/issues)
- [kyegomez/MultiModalMamba on GitHub](https://github.com/kyegomez/MultiModalMamba)
- [License: MIT](https://github.com/kyegomez/MultiModalMamba/blob/main/LICENSE)
- [Project website](https://discord.gg/GYbXvDGevY)
- [README](https://github.com/kyegomez/MultiModalMamba/blob/main/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/kyegomez-multimodalmamba