Feature Scaling

Feature scaling adjusts the range of numeric input values so that each feature contributes equally to a model's training. In practice, operators running local LLMs rarely need to scale text tokens, but if you fine-tune a model on numeric data (e.g., sensor readings alongside text), unscaled features with large ranges (e.g., 0–1000) can dominate gradient updates, slowing convergence or causing instability. Common methods include min-max scaling (squashing values to [0,1]) and standardization (centering to mean 0, variance 1). The choice matters for model accuracy and training speed.

Suppose you fine-tune a small LLM (e.g., Phi-3-mini) on a dataset mixing text reviews and numeric ratings (1–5) plus price (0–500). Without scaling, the price feature's larger magnitude would dominate the loss, making the model ignore ratings. Using scikit-learn's StandardScaler on the numeric columns before training ensures each feature contributes proportionally, often improving validation loss by 5–10%.

In a Hugging Face Transformers fine-tuning script, you'd load numeric columns, apply sklearn.preprocessing.StandardScaler, then concatenate scaled features with token embeddings. For example: scaler = StandardScaler(); scaled_nums = scaler.fit_transform(numeric_data); then pass scaled_nums as additional inputs to the model. This is common in multimodal or regression-tuned LLM workflows.