Inference
Greedy Decoding
A decoding strategy that always picks the single most likely next token at each step, with no randomness.
In depth
A decoding strategy that always picks the single most likely next token at each step, with no randomness. It is the default in Hugging Face Transformers and is roughly what you get when you set temperature to 0. Greedy decoding works well for short, factual outputs and makes runs easier to reproduce, but it tends to repeat itself on long generations. It can also miss a better overall sequence that starts with a less likely token.
Primary source: huggingface.co
Example
In practice, developers reach for Greedy Decoding when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Greedy Decoding?
A decoding strategy that always picks the single most likely next token at each step, with no randomness.
Why does Greedy Decoding matter for AI developers?
Greedy Decoding sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Greedy Decoding?
Developers Digest publishes tutorials and videos that cover Inference topics including Greedy Decoding. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
GGUF
A binary file format for storing quantized language models, designed for efficient local inference with llama.cpp and tools built on it.
Inference
Min-P Sampling
A sampling method that keeps only tokens whose probability is at least a set fraction of the most likely token's probability, then samples from what remains.
Inference
Logits
The raw, unnormalized scores a language model's output layer assigns to every token in its vocabulary at each step, before softmax turns them into probabilities.
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