Kakao Brain Releases Multimodal Language Model "Honeybee" on GitHub

Seoul, South Korea - Kakao Brain has released "Honeybee," a new open-source multimodal language model (MLLM), on GitHub.

Kakao Brain published a related paper titled "Honeybee: Locality-enhanced Projector for Multimodal LLM" on arXiv, which details the technology behind this model.

The paper highlights using a visual projector to improve the model's understanding of image data.

Honeybee has outperformed other MLLMs on MME, MMBench, and SEED-Bench benchmarks.

In particular, it scored 1977 out of 2800 on the MME benchmark, focusing on perceptual and cognitive skills.

Details and technical capabilities:

Honeybee is an advanced MLLM that integrates image and text input for comprehensive processing and response generation.

Its release is intended to encourage further development in MLLMs, a relatively nascent area with limited publicly available models and undisclosed learning methods.

MLLM, like Honeybee, extends the functionality of large language models by accepting both image and text input.

This dual input capability allows it to understand and respond to queries related to visual content or mixed media.

What comes next: 

The source and inference code for 'Honeybee' is now publicly available on GitHub.

The 'Honeybee' model is expected to be useful in education and learning applications, providing interactive capabilities when images are paired with textual questions.

Kim Il-Doo, a joint representative of Kakao Brain, emphasized ongoing research to improve AI models and potential service expansions using "Honeybee."

Kakao Brain aims to continue its research and development efforts to improve the capabilities of AI models.