Chapter 3: Environment and Library Setup

Before training a Small Language Model, you need a stable software environment. Training algorithms rely heavily on specific libraries to manage hardware resources, process text, and update neural network weights effectively. Even though SLMs require less memory than massive models, improper configuration will quickly lead to out-of-memory errors or very slow processing times.

In this chapter, you will configure the tools required for local model training. We will start by installing PyTorch with CUDA support to enable GPU hardware acceleration. These frameworks abstract away the heavy mathematics underlying neural networks. Instead of manually programming matrix multiplications like $Y = WX + b$ or writing custom functions for loss calculations such as:

$L = -\sum y_i \log(\hat{y}_i)$

you will use established libraries that handle these backend computations natively.

From there, we will look at the Hugging Face ecosystem. You will use the Transformers library to load base models and the Datasets library to process training data. We will also implement the Accelerate library to manage memory usage and distribute workloads efficiently. Finally, you will write a foundational Python script that integrates these components and prepares your system for the actual fine-tuning process.