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explainextended.com | ||
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zserge.com
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| | | | | Finally, building a simple GPT model that would finish our sentences. | |
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justinhj.github.io
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| | | | | [AI summary] The user has provided a detailed explanation of implementing the BPE (Byte Pair Encoding) algorithm for tokenization, focusing on the challenges and considerations involved in the process. They describe the use of different conflict resolution strategies, such as first occurrence and lexicographical ordering, and discuss the optimization techniques applied to improve performance, including incremental frequency counting and efficient data structures. The user also outlines future directions for the project, such as porting to Zig, exploring other tokenization algorithms, and optimizing encoding/decoding steps. The response highlights the complexity of working with C++ and the benefits of using modern C++ practices while emphasizing the importanc... | |
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haifengl.wordpress.com
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| | | | | Generative artificial intelligence (GenAI), especially ChatGPT, captures everyone's attention. The transformerbased large language models (LLMs), trained on a vast quantity of unlabeled data at scale, demonstrate the ability to generalize to many different tasks. To understand why LLMs are so powerful, we will deep dive into how they work in this post. LLM Evolutionary Tree... | |
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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 | ||
