MODEL NOTES / FROM INPUT TO OUTPUT

Sequence model · Architecture explained

Explain module roles, information flow and training versus inference.

Start with the information flow

Input sequence

Embeddings

Context transformation

Output projection

Predictions

Relations

Encoding: Input sequence → Embeddings
Vectors: Embeddings → Context transformation
Hidden representation: Context transformation → Output projection
Distribution: Output projection → Predictions

What each module does

Input and representation

Convert discrete input into representations the model can process. For a real model, specify tokenization, positional information and dimensions.

Context transformation

Combine the current position with contextual information. Follow the paper and implementation for the exact connections and computations.

Output projection

Map hidden representations to the target space. Outputs may be classes, vocabulary distributions or other structures.

Inference strategy

Turn model outputs into usable results. Explain sampling, thresholds and decoding separately from the model itself.

Check training and inference separately

Dimension	Training	Inference
Goal	Update parameters using the loss	Compute outputs with fixed parameters
Evidence	Training documentation and configuration	Inference implementation
Input	Training examples and targets	User input and context
Additional mechanisms	Optimizer and gradients	Caching and decoding

Evidence checklist

Record the paper version, configuration, module implementation and weight documentation. This deliberately simplified concept does not describe a specific model or a complete Transformer architecture. Explicitly identify any omitted mechanisms.

Using this template

Prepare your materials

Paper and model version; Official configuration; Implementation files and symbols

Keep and adapt

Preserve the logical relationships and data bindings. Adapt the sections and visual design to your readers, brand and actual materials.

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