Large Multimodal Models (LMMs) use multiple data types, including text, images, and more in their training process, thus allowing a more comprehensive understanding and processing of diverse data types. Models like Claude3, GPT-4V, and Gemini Pro Vision are more adept at handling a broad range of real-world tasks that involve text and non-text inputs. This ability to use and customize these models makes them cost-effective and scalable, offering significant potential across various industries, including healthcare, business analysis, autonomous driving, and more. However, these models have some limitations, such as their inability to process complex visual tasks due to the absence of detailed pixel-level information and object segmentation data.
Fine-tuning LMMs on domain-specific data can significantly enhance their performance for specific tasks. The LLaVA model can be fine-tuned and deployed on Amazon SageMaker, and its source code can be found on Github. The model combines pre-trained language models like Vicuna and LLaMA with visual encoders. Importantly, in preparing data for LLaVA model fine-tuning, it is crucial to have high-quality and comprehensive annotations that allow for rich representations and human-level performance proficiency in visual reasoning tasks.
Python generates various types of visual presentations, while the Amazon Bedrock LLaMA2-70B model generates text descriptions and question-answer pairs. These synthesize examples of text descriptions, question-answer pairs, and corresponding charts, which augments datasets with multimodal examples fit for specific use cases. The image-text pairs are then formatted in the JSON lines format, where each line is a training sample.
LLaVA also allows fine-tuning of all parameters of the base model or by using LoRA to tune a smaller number of parameters. Lastly, with the training model uploaded to Amazon S3, the model can then be deployed on SageMaker.
In conclusion, fine-tuning the LLaVA language model on Sagemaker for custom visual question answering tasks has shed light on the advancements made in bridging the gap between textual and visual comprehension, especially regarding tasks that require in-depth comprehension of both modalities.
