A comprehensive technical series walks through the entire process of building a multimodal large language model (MLLM) from scratch, starting from basic matrix operations. The author covers tokenizer design, pre-training strategies, and supervised fine-tuning (SFT) using a CLIP-ViT encoder integrated with a GPT-2 Medium-based text-only backbone. This resource is particularly valuable for ML engineers and researchers who want to understand the practical engineering behind modern multimodal AI, including data preparation, model architecture decisions, and training optimization. The series avoids high-level abstractions and dives into concrete implementation details, making it a rare find for those seeking to replicate or innovate on MLLM architectures.
This series details building a multimodal large language model from scratch, covering tokenization, pre-training, and SFT with a CLIP-ViT encoder. It offers valuable insights for developers looking to understand the full pipeline of modern AI systems.