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How to Build a Matryoshka-Optimized Sentence Embedding Model for Ultra-Fast Retrieval with 64-Dimension Truncation

In this tutorial, we fine-tune a Sentence-Transformers embedding model using Matryoshka Representation Learning so that the earliest dimensions of the vector carry the most useful semantic signal. We train with MatryoshkaLoss on triplet data and then validate the key promise of MRL by benchmarking ...

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NVIDIA Researchers Introduce KVTC Transform Coding Pipeline to Compress Key-Value Caches by 20x for Efficient LLM Serving

Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability, the KV cache footprint increases and becomes a major bottleneck for throughput and latency. For modern Transformers, this ...

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