otter

**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.