Phase 3: Multimodal Generation · ~120 minutes · Python
MIO and Any-to-Any Streaming Multimodal Models
GPT-4o ships a product most open models cannot replicate: an agent that hears voice, sees video, and speaks back in real time. The open-ecosystem answer by late 2024 was MIO (Wang et al., September 2024). MIO tokenizes text, image, speech, and music, trains one causal transformer over the interleaved sequences, and generates any modality to any modality. AnyGPT (Zhan et al., February 2024) was the proof of concept; MIO is the scale-up; Unified-IO 2 (Allen AI, December 2023) is the cousin with vision + action grounding. This lesson reads the any-to-any pattern — four tokenizers, one transformer, streaming-friendly decode.
What you will learn
- Design a shared vocabulary that hosts text, image, speech, and music tokens without collisions.
- Compare SEED-Tokenizer (images) and SpeechTokenizer residual-VQ (speech) on compression + reconstruction trade-offs.
- Explain the four-stage curriculum that builds up any-to-any generation.
- Name the three open any-to-any recipes and their main trade-offs: MIO, AnyGPT, Unified-IO 2.
Introduction
- Type: Learn
- Languages: Python (stdlib, four-modality token allocator + streaming decode loop)
- Prerequisites: Chameleon and Early-Fusion Token-Only Multimodal Models, the NLP & Speech Processing course
- Time: ~120 minutes
Objective
Learning objectives
- Design a shared vocabulary that hosts text, image, speech, and music tokens without collisions.
- Compare SEED-Tokenizer (images) and SpeechTokenizer residual-VQ (speech) on compression + reconstruction trade-offs.
- Explain the four-stage curriculum that builds up any-to-any generation.
- Name the three open any-to-any recipes and their main trade-offs: MIO, AnyGPT, Unified-IO 2.
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build, Check Yourself, Check Yourself, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.
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