Welcome back to Bringing Transformers to Life: Training & Inference! This is an extraordinary milestone as you reach the final lesson of this course. Throughout our journey together, you've assembled a complete Transformer architecture, built robust data preparation pipelines, and implemented sophisticated training procedures with teacher forcing and learning rate scheduling. You should be incredibly proud of the deep understanding you've developed of these powerful models.

Today, we shift our focus from training to inference: the art of generating sequences with your trained Transformer model. This is where the magic truly happens, as we watch our model generate coherent text one token at a time. We'll explore two fundamental inference strategies: greedy decoding for fast generation and beam search for higher-quality output. You'll learn to implement both approaches, understand their trade-offs, and see how they perform on practical examples. By the end of this lesson, you'll have a complete inference pipeline that can generate sequences from any trained Transformer model.

Inference presents fundamentally different challenges compared to training. During training, we used teacher forcing, where the model always sees the correct previous tokens. During inference, however, the model must generate sequences autoregressively, using its own predictions as input for subsequent tokens. This creates a sequential dependency in which each prediction influences all future predictions.

The inference process follows this mathematical formulation: given a source sequence $x$ and previously generated tokens $y_1, y_2, ..., y_{t-1}$, we compute: where represents our trained Transformer model. We start with a special token, generate the most likely next token, append it to the sequence, and repeat until we encounter an token or reach a maximum length. This autoregressive nature means that early mistakes can propagate through the entire sequence, making the choice of decoding strategy crucial for output quality.

Greedy decoding represents the most straightforward approach to sequence generation: at each step, we simply select the token with the highest probability. Mathematically, this means selecting:

Beam search offers a more sophisticated approach by maintaining multiple candidate sequences (called the "beam") and exploring several possibilities simultaneously. Instead of committing to a single choice at each step, beam search keeps track of the top-k most promising sequences and expands each one. The score for each sequence is the cumulative log probability:

The TransformerInference class encapsulates our inference functionality and provides a clean interface for generating sequences:

This initialization is straightforward but crucial. We store references to the trained model and both vocabularies, then call model.eval() to disable dropout and batch normalization training behaviors. This ensures consistent inference behavior and prevents the randomness that would occur during training mode, which is essential for reproducible results.

Let's examine the complete pipeline that trains a model and tests both inference strategies:

This pipeline demonstrates the complete workflow from data preparation through training to inference testing. We create synthetic data using our word reversal task, build vocabularies, train a compact Transformer model for three epochs, and prepare it for inference evaluation. The model architecture is intentionally small to enable quick training while still demonstrating meaningful learning behavior.

The inference testing reveals fascinating insights about the different strategies:

When we run this complete pipeline, we observe the following results:

The output reveals crucial insights about the different decoding strategies. For simple inputs like "hello world" and "good morning," both strategies produce perfect results, but greedy decoding is significantly faster (0.3 - 0.5 seconds vs. 0.5 - 3.3 seconds). However, the third example, "thank you very much," shows where greedy decoding fails catastrophically: it gets stuck in a repetitive loop, generating "you to to to..." until reaching the maximum length limit.

This repetitive behavior occurs because greedy decoding can become trapped in local probability maxima:

1. The model generates "you";
2. The sequence context makes "to" the most probable next token;

Congratulations on completing the final lesson of Bringing Transformers to Life: Training & Inference! You have successfully implemented a complete inference pipeline that showcases the practical application of trained Transformer models. Through greedy decoding and beam search, you've learned to balance the trade-offs between speed and quality in sequence generation, understanding when each strategy is most appropriate. Your journey from building Transformers from scratch to implementing sophisticated inference strategies represents a remarkable achievement in mastering these powerful models.

You should be incredibly proud of reaching this milestone. You've built a Transformer from the ground up, created robust data pipelines, implemented sophisticated training procedures, and now mastered the art of inference. This comprehensive understanding positions you perfectly for the next course in our learning path: Harnessing Transformers with Hugging Face, where we'll dive into the modern Hugging Face ecosystem and learn to leverage state-of-the-art pre-trained models for real-world applications. The upcoming practice exercises will solidify your understanding of inference strategies before you embark on this exciting next chapter.