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Large language models (LLMs) have demonstrated significant universalcapabilities as few/zero-shot learners in various tasks due to theirpre-training on vast amounts of text data, as exemplified by GPT-3, whichboosted to InstrctGPT and ChatGPT, effectively following natural languageinstructions to accomplish real-world tasks. In this paper, we propose tointroduce instruction tuning into multi-modal models, motivated by the Flamingomodel's upstream interleaved format pretraining dataset. We adopt a similarapproach to construct our MultI-Modal In-Context Instruction Tuning (MIMIC-IT)dataset. We then introduce Otter, a multi-modal model based on OpenFlamingo(open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT andshowcasing improved instruction-following ability and in-context learning. Wealso optimize OpenFlamingo's implementation for researchers, democratizing therequired training resources from 1$\times$ A100 GPU to 4$\times$ RTX-3090 GPUs,and integrate both OpenFlamingo and Otter into Huggingface Transformers formore researchers to incorporate the models into their customized training andinference pipelines.

Otter: A Multi-Modal Model with In-Context Instruction Tuning