Researchers at Intel Labs Introduce LLaVA-Gemma: A Compact Vision-Language Model Leveraging the Gemma Large Language Model in Two Variants (Gemma-2B and Gemma-7B)

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Introducing LLaVA-Gemma: A Compact Vision-Language Model

Researchers at Intel Labs have introduced LLaVA-Gemma, a suite of vision-language assistants leveraging the Gemma Large Language Model in two variants, Gemma-2B and Gemma-7B. This research provides practical insights into the trade-offs between computational efficiency and multimodal understanding in small-scale vision-language models.

Key Contributions

1. Introduction of LLaVA-Gemma, an MMFM that utilizes compact Gemma language models for efficient multimodal interactions.
2. Evaluation of Gemma-2B and Gemma-7B model variants, providing valuable insights into the trade-offs between computational efficiency and the richness of visual and linguistic understanding in LLMs.
3. Deep exploration into alternate design choices and visualization of attention with relevancy maps to enhance understanding of the model’s performance and attention.

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Researchers at Intel Labs Introduce LLaVA-Gemma: A Compact Vision-Language Model Leveraging the Gemma Large Language Model in Two Variants (Gemma-2B and Gemma-7B)

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