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raghav-singhal
Open-source 1pp-0.58b-v2-asst-seed1234-sft model from raghav-singhal — available for download and self-hosting on Hugging Face.
raghav-singhal
Open-source 1pp-0.98b-v2-raw-seed42-sft model from raghav-singhal — available for download and self-hosting on Hugging Face.
raghav-singhal
Open-source 1pp-0.58b-v2-raw-seed1234-sft model from raghav-singhal — available for download and self-hosting on Hugging Face.
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EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.