Large Language Models (LLMs) can produce varied, creative, and sometimes surprising outputs even when given the same prompt. This randomness is not a bug but a core feature of how the model samples its next token from a probability distribution. In this article, we break down the key sampling strategies and demonstrate how parameters such […]
In this article, you will learn practical ways to convert raw text into numerical features that machine learning models can use, ranging from statistical counts to semantic and contextual embeddings.
In this article, you will learn what data leakage is, how it silently inflates model performance, and practical patterns for preventing it across common workflows.
Natural language generation (NLG) is challenging because human language is complex and unpredictable. A naive approach of generating words randomly one by one would not be meaningful to humans. Modern decoder-only transformer models have proven effective for NLG tasks when trained on large amounts of text data. These models can be huge, but their structure […]
The Llama family of models are large language models released by Meta (formerly Facebook). These decoder-only transformer models are used for generation tasks. Almost all decoder-only models nowadays use the Byte-Pair Encoding (BPE) algorithm for tokenization. In this article, you will learn about BPE. In particular, you will learn: What BPE is compared to other […]
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