Multi-faceted models strive to integrate data from diverse sources, including written language, pictures, and videos, to execute various functions. These models have demonstrated considerable potential in comprehending and generating content that fuses visual and textual data.

An important component of multi-faceted models is instruction tuning, which involves fine-tuning the model based on natural language directives. This allows the model to know user intentions higher and generate precise and pertinent responses. Instruction tuning has been effectively employed in large language models (LLMs) like GPT-2 and GPT-3, enabling them to follow instructions to perform real-world tasks.

Existing approaches in multi-modal models will be categorized into system design and end-to-end trainable models perspectives. The system design perspective connects different models using a dispatch scheduler like ChatGPT but lacks training flexibility and will be costly. The tip-to-end trainable models perspective integrates models from other modalities but can have high training costs or limited flexibility. Previous instruction tuning datasets in multi-modal models lacks in-context examples. Recently, a brand new approach proposed by a research team from Singapore introduces in-context instruction tuning and constructs datasets with contextual examples to fill this gap.

The predominant contributions of this work include:

• The introduction of the MIMIC-IT dataset for instruction tuning in multi-modal models.
• The event of the Otter model with improved instruction-following and in-context learning abilities.
• The optimization of OpenFlamingo implementation for easier accessibility.

These contributions provide researchers with a priceless dataset, an enhanced model, and a more user-friendly framework for advancing multi-modal research.

Concretely, the authors introduce the MIMIC-IT dataset, which goals to boost OpenFlamingo’s instruction comprehension capabilities while preserving its in-context learning capability. The dataset consists of image-text pairs with contextual relationships, while OpenFlamingo goals to generate text for a queried image-text pair based on in-context examples. The MIMIC-IT dataset is introduced to boost OpenFlamingo’s instruction comprehension while maintaining its in-context learning. It includes image-instruction-answer triplets and corresponding context. OpenFlamingo is a framework that permits multi-modal models to generate text based on images and contextual examples.

During training, the Otter model follows the OpenFlamingo paradigm, freezing the pretrained encoders and fine-tuning specific modules. The training data follows a specific format with image, user instruction, “GPT”-generated answers, and a [endofchunk] token. The model is trained using cross-entropy loss, with the token separating solutions for prediction objectives.

The authors integrated Otter into Hugging Face Transformers, allowing easy reuse and integration into researchers’ pipelines. They optimized the model for training on 4×RTX-3090 GPUs and supported Fully Sharded Data Parallel (FSDP) and DeepSpeed for improved efficiency. Additionally they offer a script for converting the unique OpenFlamingo checkpoint into the Hugging Face Model format. Regarding demonstrations, Otter performs higher in following user instructions and exhibits advanced reasoning abilities in comparison with OpenFlamingo. It demonstrates the flexibility to handle complex scenarios and apply contextual knowledge. Otter also supports multi-modal in-context learning and performs well in visual question-answering tasks, leveraging information from images and contextual examples to offer comprehensive and accurate answers.

In conclusion, this research contributes to multi-modal models by introducing the MIMIC-IT dataset, enhancing the Otter model with improved instruction-following and in-context learning abilities, and optimizing the implementation of OpenFlamingo for easier accessibility. Integrating Otter into Hugging Face Transformers enables researchers to leverage the model with minimal effort. The demonstrated capabilities of Otter in following user instructions, reasoning in complex scenarios, and performing multi-modal in-context learning showcase the advancements in multi-modal understanding and generation. These contributions provide priceless resources and insights for future research and development in multi-modal models.

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