Multimodal input: vision and audio

Multimodal inference requires a model/runtime path that supports vision or audio behavior. GGUF models use a model plus projector pair. Native .litertlm bundles use LiteRT-LM's bundle-native media processors and do not load a separate projector.

GGUF projector flow#

await engine.loadModel('/path/to/model.gguf');
await engine.loadMultimodalProjector('/path/to/mmproj.gguf');

Use source-based loading when the projector should be resolved, downloaded, and cached like a remote model source:

await engine.loadModelSource(
  ModelSource.parse('hf://owner/repo/model-Q4_K_M.gguf'),
);
await engine.loadMultimodalProjectorSource(
  ModelSource.parse('hf://owner/repo/mmproj.gguf'),
);

Native/file-backed backends download remote projectors through the configured ModelDownloadManager before loading the cached local path. URL-loading web backends support remote unauthenticated projector URLs directly and reject local filesystem paths or options that require native cache IO such as auth headers, checksum verification, explicit cache policy changes, custom cache directories, disabled resume, and custom retry counts.

Projector offload follows effective model-load configuration. If model loading is CPU-only (preferredBackend: GpuBackend.cpu or gpuLayers: 0), projector initialization also runs CPU-only.

unloadModel() and dispose() release the projector with the model. unloadMultimodalProjector() releases only the projector and keeps the model loaded. Loading another projector replaces the active one.

LiteRT-LM bundle flow#

await engine.loadModel('/path/to/model.litertlm');

Native LiteRT-LM accepts LlamaImageContent and LlamaAudioContent backed by local paths or encoded media bytes. Remote image URLs and raw PCM Float32List audio samples are rejected before native generation because the current LiteRT-LM C message loader expects a local path or base64 blob.

Native LiteRT-LM starts audio preprocessing on the selected backend (CPU when NPU is selected). If that fails, it retries on CPU and keeps CPU audio for the loaded model; with the GPU backend, the Gemma 4 E2B bundle resolves to GPU text and vision with CPU audio.

Build multimodal message#

final message = LlamaChatMessage.withContent(
  role: LlamaChatRole.user,
  content: const [
    LlamaImageContent(path: '/path/to/image.jpg'),
    LlamaTextContent('Describe this image in one sentence.'),
  ],
);

await for (final chunk in engine.create([message])) {
  final text = chunk.choices.first.delta.content;
  if (text != null) {
    print(text);
  }
}

Capability checks#

final supportsVision = await engine.supportsVision;
final supportsAudio = await engine.supportsAudio;
final supportsVideo = await engine.supportsVideo; // false in current releases

Always prefer these runtime checks over model-card assumptions. A loaded projector can expose only a subset of the family-level modalities. The current Gemma 4 E2B GGUF projector path in native llama.cpp mtmd reports both vision and audio support; audio remains experimental upstream. Web continues to rely on the loaded bridge's runtime capability report.

Native .litertlm bundles process media themselves. loadMultimodalProjector*, supportsVision and supportsAudio apply only to GGUF projectors.

Video isn't supported; send extracted frames as LlamaImageContent. LlamaVideoContent fails with LlamaUnsupportedException.

LlamaAudioContent is generic audio input routed through normal generation; it does not by itself provide a transcript contract. For typed transcription, see Speech to Text.

Web notes#

• Web uses bridge runtime paths.
• Multimodal projector loading on web is URL-based.
• loadMultimodalProjectorSource(...) accepts remote unauthenticated projector URLs on URL-loading web backends; source options that require the native download/cache manager are unsupported there.
• Local file path media inputs are native-first; web flows use browser file bytes/URLs.
• LiteRT-LM web through @litert-lm/core remains text-only in llamadart.

Tuning notes#

• Start with smaller images or audio inputs before changing backend settings.
• The example chat app caps picked image inputs to a 384px max edge before staging them, but direct LlamaImageContent(...) usage does not resize media for you.
• Projector load success does not imply every modality is available. Re-check engine.supportsVision / engine.supportsAudio after loading mmproj.
• Keep context and generation budgets tighter than your text-only defaults.
• Follow-up turns after an image can still overflow the active context window if conversation history grows too large.
• If multimodal is unstable on GPU, establish a working CPU baseline first.
• For broader tuning workflow and diagnostics guidance, see Performance Tuning.