Training a Multimodal LLM From Scratch — Full Pipeline Guide
End-to-end guide to training multimodal LLMs covering pre-training, instruction tuning, alignment, modality fusion, and external system integration.
2026 update note
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Editorial note for 2026. This does not replace the historical article below.
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Training a Multimodal LLM From Scratch
Five-stage pipeline for training multimodal models.
Stage 1: Choose vision encoder (CLIP ViT). Stage 2: Train vision-language connector (MLP). Stage 3: Pre-train on image-caption pairs. Stage 4: Instruction tuning on multimodal datasets (LLaVA-Instruct, ShareGPT4V). Stage 5: RLHF/DPO alignment.
Hardware: 8x A100-80GB for full training. LoRA reduces to 1-2 consumer GPUs.
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