This repository provides the knowledge-distilled, ultra-compact on-device encoders (LiteRT.tflite and ONNX Runtime WebGPU.onnx) and precomputed Float32 binary vector databases (.f32) distilled from the full 740M teacher model (jaichang/embeddinggemma2-global-birds-2000). Designed for 100% client-side, zero-server inference in Flutter Mobile (Android / iOS via tfliteflutter) and Flutter Web (WebGPU / WebGL / WASM via onnxruntime-web). Instead of downloading the 1.2 GB model.safetensors onto a phone or browser, we trained lightweight student networks to directly regress the exact 768-dimensional L2-normalized embedding coordinates of EmbeddingGemma 2 (740M) using Cosine + InfoNCE Distillation…
Independent publisher
Jeffrey
jaichang
Models in Library2
Datasets in Library0
Models on Hugging Face7
Followers1
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Models
This repository provides the knowledge-distilled, ultra-compact on-device encoders (LiteRT.tflite and ONNX Runtime WebGPU.onnx) and precomputed Float32 binary vector databases (.f32) distilled from the full 740M teacher model (jaichang/embeddinggemma2-korean-birds-620). Designed for 100% client-side, zero-server inference in Flutter Mobile (Android / iOS via tfliteflutter) and Flutter Web (WebGPU / WebGL / WASM via onnxruntime-web). Instead of downloading the 1.2 GB model.safetensors onto a phone or browser, we trained lightweight student networks to directly regress the exact 768-dimensional L2-normalized embedding coordinates of EmbeddingGemma 2 (740M) using Cosine + InfoNCE Distillation…