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boring-guy.sh | ||
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comsci.blog
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| | | | | In this tutorial, we will implement transformers step-by-step and understand their implementation. There are other great tutorials on the implementation of transformers, but they usually dive into the complex parts too early, like they directly start implementing additional parts like masks and multi-head attention, but it is not very intuitional without first building the core part of the transformers. | |
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sigmoidprime.com
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| | | | | An exploration of Transformer-XL, a modified Transformer optimized for longer context length. | |
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www.paepper.com
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| | | | | Introduction LoRA (Low-Rank Adaptation of LLMs) is a technique that focuses on updating only a small set of low-rank matrices instead of adjusting all the parameters of a deep neural network . This reduces the computational complexity of the training process significantly. LoRA is particularly useful when working with large language models (LLMs) which have a huge amount of parameters that need to be fine-tuned. The Core Concept: Reducing Complexity with Low-Rank Decomposition | |
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lakefs.io
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| | | An update on all the latest tools and trends in the data engineering space for 2022. | ||
