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
Older publish date · context add-on
Editorial note for 2026. This does not replace the historical article below.
- Prefer current official docs for frameworks, APIs, and package names; sample code here is mostly pedagogical—check release notes when migrating.
- Llama / RAG / Prompt ecosystems move fast; pair this post with 2026 articles and the tools directory on this site.
- If you spot factual drift, reach out via the footer—we will refresh this note or spin up a follow-up post.
Recent picks: Observability · Graph RAG · Prompting & tools · Small-model deployment
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.
Reuse: licensed under CC BY 4.0 — quoting, summarizing, and republishing is permitted with attribution.
Cite as: “Training a Multimodal LLM From Scratch — Full Pipeline Guide,” AI Dev Hub, https://aidevhub.net/blog/training-multimodal-llm
Markdown for AI/LLM consumption: /blog/training-multimodal-llm.md · full corpus: /llms-full.txt
Related Content
Articles
Distributed Training Techniques for Large Language Models
Comprehensive survey of distributed LLM training approaches including data parallelism, tensor parallelism, pipeline parallelism, and ZeRO optimization.
Read moreArchitecture Guide for Building an LLM Application Platform
Comprehensive architectural overview of designing and deploying a production LLM application platform covering RAG, agent systems, and monitoring.
Read moreFrom LLMs to AI Agents — 25 Papers on Agentic Workflows
Comprehensive survey of 25 landmark papers tracing the evolution from simple LLM workflows to autonomous agentic systems and multi-agent architectures.
Read moreRelated Tools
chatgpt
Conversational AI assistant by OpenAI for real-time code generation, debugging, and technical problem-solving through natural language interaction.
View toolWindsurf
Codeium AI-native IDE with Cascade agentic flow, multi-file editing, deep codebase indexing, and real-time collaborative AI assistance.
View toolClaude Code
Anthropic's terminal-native AI coding agent that operates directly on your codebase with file editing, shell command execution, and Git integration.
View toolRelated Workflows
AI-Powered Code Review Workflow
Use AI tools to automate and improve your code review process
View workflowBuilding with MCP: Server Development Workflow
Step-by-step workflow for creating and deploying MCP servers
View workflowChatGPT Prompt Engineering Workflow
Master prompt engineering techniques to get the best results from ChatGPT
View workflow