Uniform Scalar Quantization compresses blockchain data, while Matryoshka Representation Learning creates nested, multi-level indexes. Combining these could revolutionize web3 indexing: smaller storage footprints, faster queries, and adaptive precision. Ideal for DEXs, marketplaces, and DeFi protocols handling large-scale, real-time data
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Introducing the Nonlinear Schrödinger Network 🐱💻 a novel physics-based AI model that integrates physics with AI to learn complex patterns, offering a more interpretable and parameter-efficient alternative to traditional deep neural networks. Achieves high accuracy in time series classification while reducing computational costs and providing insights into data dynamics
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Q-GaLore is a game-changer for training Large Language Models (LLMs). By combining quantization and low-rank projection, it slashes memory usage without sacrificing performance. Train a LLaMA-7B model on just a single NVIDIA RTX 4060 Ti (16 GB memory) and cutting fine-tuning memory by 50%!
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