This is Horizon-Labs/multilingual-reranker-small in a smaller ONNX file, for running on a CPU. Same model, same tokenizer, same scores to within a small margin. The file is about a third smaller than the original quantized one. We use it in Sno Station to rerank memories for coding agents. The token embeddings, the attention projections and the first MLP projection are stored in 8 bits. The second MLP projection stays in float. Its inputs carry a few very large values, and 8 bits move the scores too much there. Weights are unsigned 8-bit, per channel. Signed weights overflow on AVX2 CPUs without VNNI and flatten every score. quantize.py rebuilds the file from the original onnx/model.onnx at…
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