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.