Summary of Llava-gemma: Accelerating Multimodal Foundation Models with a Compact Language Model, by Musashi Hinck et al.
LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model
by Musashi Hinck, Matthew L. Olson, David Cobbley, Shao-Yen Tseng, Vasudev Lal
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The researchers train a suite of multimodal foundation models (MMFM) using the LLaVA framework with large language models from the Gemma family. They test the effect of ablating three design features: pretraining the connector, utilizing a more powerful image backbone, and increasing the size of the language backbone. The resulting models, called LLaVA-Gemma, exhibit moderate performance on various evaluations but fail to improve past similarly sized state-of-the-art (SOTA) models. The analysis shows mixed effects; skipping pretraining tends to reduce performance, larger vision models sometimes improve performance, and increasing language model size has inconsistent effects. The researchers publicly release training recipes, code, and weights for their LLaVA-Gemma models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper trains many kinds of computer models that can understand different types of information. They use big computers to train these models and see how they do on various tasks. The results show that the models are okay but not really better than what’s already out there. It looks like some things help and some don’t, but overall it’s an interesting area of research. |
Keywords
» Artificial intelligence » Language model » Pretraining
