EmbeddingGemma-2-GGUF

☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. EmbeddingGemma 2 is a multilingual, multimodal embedding model from Google DeepMind. It maps text and code, images, video, and audio into a shared 768-dimensional vector space and has an 8,192-token shared context window. The upstream checkpoint has a 270M-parameter text path plus optional vision and audio encoders. This release contains a quantized text backbone and a shared BF16 vision/audio projector. Text, image, and audio inputs returned normalized 768-dimensional vectors in local `llama-server` CPU smoke checks. See the official model card for supported task prefixes, modalities, and input guidance. Every value below comes from local measurements of the locked BF16 GGUF and quantized files; the numbers are not copied from the upstream model card. The fixed task evaluation uses the 1,379-pair test split of MTEB STSBenchmark STS, dataset revision `96943a16ea6a35129e253c659081cb59daf81b30`. It covers 2,552 unique sentences with the `task: sentence similarity | query:` prefix, an 8,192-token context, and the CPU `llama.cpp` runtime at commit `9c2e0e491a822adae1f0b1c831adb4160057d24f`. Spearman and Pearson measure correlation between cosine similarity and human scores. Mean and 5th-percentile cosine measure vector agreement with the BF16 GGUF reference. Higher values indicate stronger task correlation or closer vector agreement; smaller files use less disk. This is a task-specific embedding evaluation, not the full MTEB benchmark. Token-level KLD, next-token Top-1, PPL, and RMS probability deltas do not apply to this embedding model.

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