Optimize Your Embedding Model with Vespa
Selecting the right embedding model is crucial for anyone using Vespa. Our latest findings reveal significant trade-offs that can enhance your hybrid search capabilities. Here’s what we discovered:
- Ranking Models: Focus on models under 500M parameters that are open-source, and have ONNX weights available.
- Inference Speed: Achieve up to 4x faster query performance through intelligent model quantization:
- INT8 models deliver remarkable speed boosts on CPU.
- FP16 distinctions outperform INT8 on GPU.
Key Findings:
- Storage & Precision: Binary vectors can reduce memory usage by 32x while retaining up to 98% quality.
- Hybrid Retrieval: Combining BM25 with vector rankings yields an average quality boost of 3-5%.
For those seeking a deeper understanding, our interactive leaderboard showcases comprehensive benchmarks.
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