# Multimodal Models

Tensormorph's per-architecture recognition layer (`ModelAdapter`) is confirmed in its design documentation; the specific set of shipped adapters for individual multimodal architectures is not - treat named architectures below as illustrative, not a confirmed support list.

A multimodal model - vision-and-text, audio-and-text, or more towers still - usually isn't one uniform stack of layers; it's several sub-networks (a vision tower, a text tower, an audio encoder) that meet at one or more fusion points. Tensormorph doesn't require a special mode to open one, but a few things behave differently once a model has more than one input modality.

## Multiple towers, one address space

Every recognized architecture is read into the Semantic Model Graph by a `ModelAdapter` written for that architecture; a multimodal model's adapter is what tells Tensormorph "these layers are the vision tower, these are the text tower, this module is where they fuse." In the Outliner and the [Architecture view](/docs/editors/architecture-editor), each tower appears as its own top-level branch of the model, with fusion modules positioned where they structurally sit - so you can navigate "the vision tower" as a coherent unit rather than an unlabeled prefix of a flat layer list.

If Tensormorph has no dedicated adapter for a given multimodal architecture, the fallback adapter still opens it - every tensor is inspectable, the address space still applies - but layers aren't automatically grouped by modality; you're navigating a flat structure rather than tower-aware one.

## Shape semantics differ by modality

The same rank-3 or rank-4 tensor shape means something different depending on which tower it belongs to - a sequence-of-tokens axis in a text tower is not the same kind of axis as a patch-grid axis in a vision tower, even when the numbers happen to match. This is why shape alone, without knowing which tower a tensor belongs to, is not enough to safely align two multimodal checkpoints for comparison: [Comparing Two Model Revisions](/docs/get-started/compare-two-models) needs tower-aware alignment for a multimodal pair, not just shape-matching.

## Inspecting a fusion point

A fusion module - where a vision tower's output and a text tower's embeddings combine - is often the most informative place to inspect in a multimodal model, since it's where cross-modal behavior actually happens. It shows up in the Architecture view like any other module; the Inspector panel and [Scalar Inspector](/docs/editors/scalar-inspector) work on it identically to a single-tower model, since neither depends on which tower a tensor came from.

## Related resources

-   [Model, Architecture, Layer and Module](/docs/core-concepts/model-architecture-layer-module) - how the `ModelAdapter`/fallback-adapter split builds the address space this page assumes.
-   [Comparing Two Model Revisions](/docs/get-started/compare-two-models) - alignment considerations specific to multi-tower models.
-   [Mixture-of-Experts Models](/docs/working-with-models/mixture-of-experts-models) - another case where architecture-aware recognition unlocks dedicated views beyond the generic fallback.