Design training for multimodal embedding model

You need to train a multimodal LLM-based system that produces multimodal embeddings (e.g., a shared vector space where text, images, and optionally audio/video can be compared).

Design the end-to-end approach:

1. Goal and use cases : What will the embeddings be used for (retrieval, clustering, classification, grounding, RAG, recommendations)? What properties must they have (alignment across modalities, robustness, latency)?
2. Model architecture :
   • How you encode each modality (vision encoder, text encoder/LLM, adapters/projectors).
   • Whether you use a single encoder, dual encoder, or encoder-decoder setup.
   • How you obtain a fixed-size embedding (CLS token, mean pooling, learned pooler, last-layer projection).
3. Training data :
   • Types of supervision (image-caption pairs, interleaved multimodal docs, instruction data, click logs).
   • Negative sampling strategy and handling noisy labels.
4. Objectives / losses :
   • Contrastive (InfoNCE), matching losses, generative objectives, distillation, multi-task setups.
   • How to balance losses across modalities.
5. Evaluation :
   • Offline metrics (Recall@K, nDCG, MRR, zero-shot classification, robustness tests).
   • Online metrics if used in a product.
6. Deployment considerations :
   • Embedding index (ANN), latency, batch vs streaming, cache.
   • Versioning/backfill of embeddings; drift monitoring.

Provide a concrete proposal, justify trade-offs, and call out key failure modes.