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Cataloged from litert-community/embeddinggemma-2-text-270m-litert-lm
All models by litert-community →Main Model Card: google/embeddinggemma-2
EmbeddingGemma 2 Text 270M is a highly efficient, text-only variant of the EmbeddingGemma 2 model family, optimized for low-latency deployment on consumer hardware and edge devices via LiteRT. With a footprint of 270M parameters (combining a 130M transformer with a 140M embedder), this model delivers high-quality text representations. It supports over 100 languages, programming code, and features Matryoshka Representation Learning (MRL) for adjustable vector dimensions (down to 128d) to dramatically lower vector storage overhead. This variant is ideal for text-centric on-device applications, including document search, classification, clustering, and question answering.
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MediaPipe uses EmbeddingGemma 2 and LiteRT-LM to enable high-level, cross-platform tasks which you can experience firsthand with the following interactive web demos:
Additional Notes:
The text performance was measured by loading and running the 128 text signature. The latency reported is the average of 5 iterations.
Memory was measured with each platform's native metrics and is not directly comparable across operating systems. CPU memory was measured using, rusage::ru_maxrss on Android, Linux and IOT, task_vm_info::phys_footprint on iOS and MacBook, and process_memory_counters::PrivateUsage on Windows. With the exception of task_vm_info::phys_footprint, accelerator (GPU/NPU/TPU) memory is not included. Memory measurements were taken from the second load which reads from loading caches. Memory usage on the first load may vary.
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IoT