With the growth of trending ai applications, Machine Learning ML models are being used for various purposes, leading to a rise in the arrival of multi-modal models. Multimodal models are very useful and researchers are putting a lot of emphasis on them nowadays as they help reflect the complexity of human cognition by integrating various data sources such as text and images. Furthermore, these models are valuable in various applications across multiple domains.
ai researchers have come up with a new multimodal model called Fuyu-Heavy. It is the third largest multimodal model in the world; only GPT4-V and Gemini Ultra are ahead but they surpassed Gemini Pro in multimodal language understanding (MMLU) and multimodal model understanding (MOU). The researchers emphasize that the model is smaller than its counterparts but demonstrates commendable performance on various benchmarks. The researchers highlight that the development of Fuyu-Heavy needed to have a balance between language and image modeling tasks. To do this, they tested and used specialized methodologies for optimal performance at scale.
In his recent ai/blog/adept-fuyu-heavy”>blog post, Adept ai researchers highlighted that the formulation of Fuyu-Heavy was a major challenge. The very scale of developing such a large model created many challenges. Furthermore, the complex task of training a novel architecture from textual and visual data posed many challenges. Additionally, training image data placed substantial pressure on the systems, requiring management of data influx, memory utilization, and cloud storage bandwidth.
Additionally, researchers needed more high-quality image pre-training data, which posed an additional challenge. This forced the researchers to formulate innovative dataset methods and therefore used existing resources and synthetically generated data for the model's image processing capabilities. Furthermore, handling coordinate systems during the training and inference stages and various image formats presented formidable challenges. To address these challenges, researchers had to pay attention to details and rigorous quality assurance measures.
The researchers tested the model on several benchmarks. They found that it outperforms many larger models within its computing class and performs equally well on many other large models, demonstrating the accuracy and capability of this model. Furthermore, they found that Fuyu-Heavy Chat proved to be effective in conversational ai, having similar capabilities to its larger counterparts like Claude 2.0 on widely used chat evaluation platforms such as MT-Bench and AlpacaEval 1.0.
They emphasized that they would focus on improving the capabilities of the base model in the future. According to him ai/blog/adept-fuyu-heavy”>blog post, the research team is studying how to convert these base models into useful agents using reward models, self-play, and various inference-time search techniques. They also focus on connecting these models to create useful and reliable products. The ability of this model to integrate text and image processing tasks shows its potential in various domains. As researchers work to improve the effectiveness and capabilities of this model, practical applications of Fuyu-Heavy will increase.
Rachit Ranjan is a consulting intern at MarktechPost. He is currently pursuing his B.tech from the Indian Institute of technology (IIT), Patna. He is actively shaping his career in the field of artificial intelligence and data science and is passionate and dedicated to exploring these fields.
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