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Training a recommendation model with dynamic embeddings noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog

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​ Posted by Thushan Ganegedara (GDE), Haidong Rong (Nvidia), Wei Wei (Google) Modern recommenders heavily leverage embeddings to create vector representations of each user and candidate item. These embedding can then be used to calculate the similarity between users and items, so that users are… Read More »Training a recommendation model with dynamic embeddings noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog

Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

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​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

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​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

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​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

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​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

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​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost

  • by

​ High deployment costs are a growing worry as huge foundation models (e.g., GPT-3.5/GPT-4) (OpenAI, 2023) are deployed in many practical contexts. Although quantization, pruning, compression, and distillation are useful general methods for lowering LLMs’ serving costs, the inference efficiency bottleneck of transformer-based generative models… Read More »Microsoft Research Propose LLMA: An LLM Accelerator To Losslessly Speed Up Large Language Model (LLM) Inference With References Tanushree Shenwai Artificial Intelligence Category – MarkTechPost