Decoding Strategies and Output Control

A detailed walkthrough of how language models generate text through decoding strategies. Covers greedy decoding, temperature sampling, top-k and nucleus (top-p) sampling, repetition penalties, beam search, stop conditions, and structured output constraints. Each strategy is explained with code examples using Hugging Face Transformers and GPT-2, including trade-offs between determinism, diversity, quality, and inference cost. Constrained decoding for JSON and fixed-label outputs is also discussed, along with pointers to key research papers.

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