Decode with TopK Sampling
Medium · Solved in PyTorch · Machine learning coding practice
Problem
In Large Language Model (LLM) inference, Top-K Sampling is a popular decoding strategy that balances quality and diversity. Unlike greedy decoding which always selects the most probable token, top-k sampling introduces controlled randomness by sampling from the k most likely tokens.
This approach prevents sampling from very unlikely tokens (which could produce nonsensical text) while still allowing diversity among reasonable choices.
Topics: inference, sampling, autoregressive
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