Logits

Logits are the raw, unnormalized scores produced by a language model's final linear layer before any probability conversion. For each position in the vocabulary, the model outputs a single real-valued logit indicating its relative preference for that token as the next prediction. These scores are fed into softmax to produce a probability distribution. Directly manipulating logits — by adding biases or masking tokens — enables precise control over generation without retraining the model.