This article provides a detailed, step-by-step explanation of how the GPT-2 decoder model predicts the next word. It traces the journey of a single vector through the model's layers, illustrating each matrix multiplication and parameter count. The explanation emphasizes that a decoder layer rewrites a fixed-width vector in place rather than compressing or replacing tokens, ultimately producing a probability distribution for the next word. AI
IMPACT Provides a foundational understanding of transformer decoder mechanics, relevant for researchers and developers working with LLMs.
RANK_REASON The article details the internal mechanics of a specific, older language model (GPT-2) for educational purposes, akin to a technical paper or deep-dive analysis. [lever_c_demoted from research: ic=1 ai=1.0]
- attention
- decoder
- decoder layers
- GPT-2
- GPT-2 base
- lookup table
- matrix multiply unit
- probability distribution
- 词元（Token）云服务提质赋能评估计划
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