autoregressive generation

Autoregressive generation is a sequence modeling approach in which a model produces output one token at a time, and each new token is predicted based on all the tokens that came before it.

In language modeling, these systems are typically trained with next-token prediction under teacher forcing, where the model learns to predict the next token given a ground-truth prefix from real data.

At inference time, common decoding strategies include greedy search, beam search, top-k sampling, and nucleus (top-p) sampling, often combined with a temperature parameter to control how random or deterministic the outputs are.

Related Resources

Tutorial

Hugging Face Transformers: Leverage Open-Source AI in Python

As the AI boom continues, the Hugging Face platform stands out as the leading open-source model hub. In this tutorial, you'll get hands-on experience with Hugging Face and the Transformers library in Python.

intermediate ai

For additional information on related topics, take a look at the following resources:

• Hugging Face Transformers (Quiz)