HyperGAI Introduces HPT: A Groundbreaking Family of Leading Multimodal LLMs

 HyperGAI Introduces HPT: A Groundbreaking Family of Leading Multimodal LLMs

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Introducing HPT: A Groundbreaking Family of Leading Multimodal LLMs

HyperGAI researchers have developed Hyper Pretrained Transformers (HPT), a multimodal language model that can handle various types of inputs such as text, images, videos, and more. The HPT model aims to deliver high performance across different input formats without significantly increasing computational costs.

Key Features of HPT:

  • HPT offers a new approach by leveraging a multimodal pretraining framework capable of training large models proficient in understanding various modalities.
  • HPT introduces two versions: HPT Pro, designed for complex multimodal tasks, and HPT Air, an efficient yet capable model for a wide range of tasks.
  • HPT employs the H-Former, a key innovation bridging vision and language modalities by converting visual data into language tokens.
  • HPT Pro outperforms larger proprietary models like GPT-4V and Gemini Pro on benchmarks such as MMBench and SEED-Image, showcasing its superiority in complex multimodal tasks.
  • HPT Air achieves state-of-the-art results among open-source multimodal LLM models of similar or smaller sizes on challenging benchmarks like MMMU, highlighting its efficiency and effectiveness.

In conclusion, the HPT framework presents a significant advancement in the field of multimodal LLMs by effectively bridging the gap between vision and language modalities, demonstrating superior performance compared to existing models on various benchmarks.

For more information, visit the Blog, explore the Model, and access the code on Github.

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