Summary of Towards Multi-modal Mastery: a 4.5b Parameter Truly Multi-modal Small Language Model, by Ben Koska et al.
Towards Multi-Modal Mastery: A 4.5B Parameter Truly Multi-Modal Small Language Model
by Ben Koska, Mojmír Horváth
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel 4.5B parameter small language model capable of handling various input and output modalities, including text, images, videos, and audio. Despite its modest size, this model achieves near state-of-the-art performance on diverse tasks, demonstrating the potential for multi-modal models to tackle complex real-world problems. The approach builds upon recent advancements in language modeling and multi-task learning to create a versatile and high-performing model suitable for edge inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shares a new small language model that can understand different types of data like text, pictures, videos, and sound. It’s really good at doing lots of tasks and might help solve big problems in the real world. The researchers combined ideas from recent breakthroughs to create a helpful model that can even be used on devices. |
Keywords
» Artificial intelligence » Inference » Language model » Multi modal » Multi task
