MiniMind-O is a research codebase for training and evaluating a small-scale (0.1B parameter) omni-modal language model that processes text, audio, and image inputs and generates text or streaming speech. The architecture uses a Thinker–Talker dual-path design with frozen external encoders (SenseVoice, SigLIP2, Mimi codec, CAM++) and trainable projectors, plus a Multi-Token Prediction (MTP) head for audio codec tokens. The repository includes a complete training pipeline (SFT with DDP, mixed precision, checkpointing), a comprehensive evaluation CLI, a Gradio web demo, and a real-time Flask WebSocket demo with VAD and ASR for near-duplex interaction. Data is stored in parquet format with pre-encoded audio codes, images, and speaker embeddings. The project is primarily research-oriented, aiming to provide a transparent, reproducible baseline for omni-modal models, but also includes application-like components for interactive demos.