Otter is a multi-modal model optimized for in-context instruction tuning — designed to handle multi-turn conversations and follow complex instructions involving multiple images and video frames, building upon the OpenFlamingo architecture.

What Is Otter?

• Definition: An in-context instruction-tuned VLM.
• Base: Built on OpenFlamingo (open-source reproduction of DeepMind's Flamingo).
• Dataset: Trained on MIMIC-IT (Multimodal In-Context Instruction Tuning) dataset.
• Capability: Can understand relationships across multiple images (e.g., "What changed between these two photos?").

Why Otter Matters

• Context Window: Unlike LLaVA (single image), Otter handles interleaved image-text history.
• Video Understanding: Can process video as a sequence of frames due to its multi-image design.
• Instruction Following: Specifically tuned to be a helpful assistant, reducing toxic/nonsense outputs.

Otter is a conversational visual agent — moving beyond "describe this picture" to "let's talk about this photo album" interactions.