Show HN: We beat Gemini Embedding 2 by training only 16M params (open weights)

Category: ai-ml

Tags: embeddings, open-weights, nlp

Score: 5.7/10 (Innovation: 6, Technical: 6, Documentation: 2, Utility: 5)

This project claims to offer an embedding model that outperforms Gemini Embedding 2 using only 16 million parameters, making it efficient and lightweight. It is open-weight on Hugging Face, targeting developers needing high-performance embeddings for semantic search or retrieval-augmented generation. The lack of README details and demonstration limits its immediate usability.

Target audience: data engineers, ml engineers

Repository: https://huggingface.co/EximiusLabs/fusion-embedding-1-2b-preview

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