A user on the r/LocalLLaMA subreddit has been experimenting with the recently released Embedding Gemma 2 model to search for text within images. The user found that for its size (330MB at Q8 with a 768 embedding dimension), the model is quite usable, demonstrating an understanding of word meaning across languages, though with lower similarity scores. The tests suggest that text content appears to dominate cosine similarity more than visual traits, and specific word existence can be more influential than entire sentences in Korean queries. AI
IMPACT Demonstrates a practical application for embedding models in image-based text retrieval.
RANK_REASON User experiment with an existing model release for a specific application.
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