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KIVI: A Plug-and-Play 2-bit KV Cache Quantization Algorithm without the Need for Any Tuning
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KIVI: A Plug-and-Play 2-bit KV Cache Quantization Algorithm without the Need for Any Tuning

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Researchers at Stanford Propose a Family of Representation Finetuning (ReFT) Methods that Operates on a Frozen Base Model and Learn Task-Specific Interventions on Hidden Representations
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Researchers at Stanford Propose a Family of Representation Finetuning (ReFT) Methods that Operates on a Frozen Base Model and Learn Task-Specific Interventions on Hidden Representations

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This AI Paper from Peking University and ByteDance Introduces VAR: Surpassing Diffusion Models in Speed and Efficiency
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This AI Paper from Peking University and ByteDance Introduces VAR: Surpassing Diffusion Models in Speed and Efficiency

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This AI Paper from SambaNova Presents a Machine Learning Method to Adapt Pretrained LLMs to Recent Languages
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This AI Paper from SambaNova Presents a Machine Learning Method to Adapt Pretrained LLMs to Recent Languages

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A Comparative Study of In-Context Learning Capabilities: Exploring the Versatility of Large Language Models in Regression Tasks
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A Comparative Study of In-Context Learning Capabilities: Exploring the Versatility of Large Language Models in Regression Tasks

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Autonomous Domain-General Evaluation Models Enhance Digital Agent Performance: A Breakthrough in Adaptive AI Technologies
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Autonomous Domain-General Evaluation Models Enhance Digital Agent Performance: A Breakthrough in Adaptive AI Technologies

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Top Data Analytics Books to Read in 2024
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Top Data Analytics Books to Read in 2024

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The Way forward for Neural Network Training: Empirical Insights into μ-Transfer for Hyperparameter Scaling
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The Way forward for Neural Network Training: Empirical Insights into μ-Transfer for Hyperparameter Scaling

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Google AI Introduces CodecLM: A Machine Learning Framework for Generating High-Quality Synthetic Data for LLM Alignment
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Google AI Introduces CodecLM: A Machine Learning Framework for Generating High-Quality Synthetic Data for LLM Alignment

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This Study by UC Berkeley and Tel Aviv University Enhances Task Adaptability in Computer Vision Models Using Internal Network Task Vectors
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This Study by UC Berkeley and Tel Aviv University Enhances Task Adaptability in Computer Vision Models Using Internal Network Task Vectors

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