gety-embed-v0

Fine-tuned from intfloat/multilingual-e5-small using open-source and proprietary synthetic data, optimized for local search scenarios.

ONNX

File	Quantization	Size
onnx/model_uint8.onnx	UINT8 Dynamic	112 MB

ONNX CUDA EP

File	Quantization	Size
onnx_cuda/model_fp16.onnx	FP16	224 MB

Inputs input_ids + attention_mask; masked mean pooling + L2 normalization baked into the graph, output is the normalized 384-dim embedding.

Core ML

File	Quantization	Size
coreml/model_fp16.mlpackage	FP16	224 MB

Single input_ids input (dynamic 1-512 tokens, no padding), pooling and normalization baked in. macOS 13+, ANE accelerated.

OpenVINO (Intel NPU)

File	Quantization	Size
openvino/model.xml + openvino/model.bin	INT8 weight-only (NNCF), FP16 activations	114 MB

Single-input OpenVINO IR for static-shape NPU compilation (reshape([1, L]) + compile, batch=1, sequence buckets e.g. 32/64/128/256/512):

• Input: input_ids [batch, seq] (int64), padded to the bucket length with <pad> (token id 1 — not config.json's pad_token_id=0, which is <s>)
• The attention mask is computed in-graph as input_ids != 1
• Masked mean pooling + L2 normalization baked in
• Output: embeddings [batch, 384] (float32)

Selected over FP16 / static-PTQ INT8 / INT4 on NPU hardware (Intel AI Boost, OpenVINO 2026.2.1): equal latency to all other variants (NPU latency is shape-bound), best accuracy (min cosine 0.9998 vs PyTorch reference; static PTQ drops to 0.9872 on short queries), and half the size of FP16.