Building Large Language Models from Scratch: A Deep Learning Guide Balancing Theory and Practice

This article introduces the open-source project llm-from-scratch, which provides a complete tutorial for building large language models from scratch. It covers theoretical foundations, architecture design, training processes, and application practices, helping developers deeply understand the internal mechanisms of LLMs. It is suitable for learners who want to build a runnable model with their own hands.

The llm-from-scratch project is created and maintained by developer ashworks1706. Its core philosophy is to understand LLMs from first principles. Unlike tutorials that only provide pre-trained models or API calls, this project requires building a complete Transformer architecture step by step from basic neural network components, making abstract concepts (such as attention mechanisms) concrete and tangible, which has unique educational value.

Transformer: The Cornerstone of Modern LLMs

• Self-Attention Mechanism: Assigns weights by calculating the similarity between Query, Key, and Value, enabling parallel processing of sequences
• Multi-Head Attention: Splits attention computation into multiple "heads" to capture different semantic relationships
• Positional Encoding: Addresses the position insensitivity issue of Transformers; compares sine encoding and learnable embeddings

Other Components

• Feed-Forward Network: Expands and contracts dimensions to provide non-linear representation
• Layer Normalization + Residual Connection: Ensures stable training of deep networks

Data Preprocessing

• Text cleaning to remove noise; compares space-based tokenization and BPE subword tokenization

Pre-training Objectives

• Uses autoregressive paradigm (predicting the next token) with cross-entropy loss

Optimization Strategies

• Adam optimizer for adaptive learning rate adjustment
• Learning rate warm-up + cosine annealing to stabilize the training process

Fine-tuning and Deployment

• After pre-training, fine-tune to adapt to downstream tasks (text classification, question answering, etc.)
• Inference optimization: quantization compression, KV cache acceleration, batch processing to improve GPU utilization

Cutting-edge Exploration

• Mentions modern LLM technologies such as RoPE positional encoding, SwiGLU activation, RMSNorm, and GQA

Target Audience

• Deep learning beginners, algorithm engineers, researchers, and tech enthusiasts

Learning Path

• Solidify mathematical foundations → Build step by step → Hands-on practice and trial-and-error → Compare with framework implementations

Common Challenges

• Gradient vanishing/explosion: Mitigated with residual connections
• Insufficient memory: Gradient accumulation + mixed-precision training
• Unstable training: Monitor curves + debugging techniques

llm-from-scratch represents the learning philosophy of "true understanding comes from hands-on building". It helps learners master the core ideas of Transformers and lays the foundation for future innovation. Project link: https://github.com/ashworks1706/llm-from-scratch Keywords: Large Language Model, Transformer, Deep Learning, Self-Attention Mechanism, Neural Network, PyTorch, Natural Language Processing, Machine Learning