USER-BGE-M3-ONNX-INT8

🧩 DeepVK-USER-BGE-M3 — Quantized ONNX (INT8)

✨ This repository contains a quantized INT8 ONNX version of deepvk/USER-bge-m3.
It is designed for fast CPU inference with ONNX Runtime, making it a great choice for semantic search, embeddings generation, and text similarity tasks in Russian 🇷🇺 and English 🇬🇧.

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🔍 Model Card

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🚀 Features

• ⚡ Fast CPU inference — ONNX + INT8 gives a strong speed-up.
• 📦 Lightweight — reduced model size, lower memory footprint.
• 🔄 Drop-in replacement — embeddings compatible with the FP32 version.
• 🌍 Multilingual — supports Russian 🇷🇺 and English 🇬🇧.

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🧠 Intended Use

✅ Recommended for:

• Semantic search & retrieval systems
• Recommendation pipelines
• Text similarity & clustering
• Low-latency CPU deployments

❌ Not ideal for:

• Absolute maximum accuracy scenarios (INT8 introduces minor loss)
• GPU-optimized pipelines (prefer FP16/FP32 models instead)

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⚖️ Pros & Cons of Quantized ONNX

Pros ✅

• Easy to use (no calibration dataset required).
• Smaller & faster than FP32.
• Works out of the box with ONNX Runtime.

Cons ❌

• Slight accuracy drop compared to static quantization.
• AVX512 optimizations only on modern Intel CPUs.
• No GPU acceleration in this export.

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📊 Benchmark

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📂 Files

model_quantized.onnx — quantized model

tokenizer.json, vocab.txt, special_tokens_map.json — tokenizer

config.json — model config

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🧩 Examples

You can try the model directly in Google Colab:

This notebook demonstrates:

• Loading the original FP32 model deepvk/USER-bge-m3
• Loading the quantized INT8 ONNX model skatzR/USER-BGE-M3-ONNX-INT8
• Comparing quality (cosine similarity) and inference speed side by side

You can try this model with ready-to-use scripts in the examples folder:

• quantmodel.py — universal Python module for loading and encoding texts with the quantized ONNX model.
• app-console.py — console script to compare FP32 vs INT8 embeddings (cosine similarity + inference time).
• app-streamlit.py — interactive demo with Streamlit.