Building an LLM – Part 1: Decoder-Only Models

By Ali Shamsaddinlou

llmtransformerdecoderarchitecturemachine-learningresearchacademic

Building an LLM – Part 1: Decoder-Only Models

Abstract

This paper presents a comprehensive analysis of decoder-only transformer architectures, the foundational components behind modern Large Language Models (LLMs). We examine the theoretical foundations, architectural design principles, training methodologies, and scaling considerations that have enabled the development of models such as GPT, LLaMA, and PaLM. Our analysis covers the evolution from early transformer designs to current state-of-the-art implementations, providing insights into the mechanisms that drive autoregressive language generation and the challenges associated with training large-scale models.

Keywords: Large Language Models, Transformer Architecture, Decoder-Only Models, Autoregressive Generation, Neural Language Modeling

1. Introduction

The emergence of decoder-only transformer architectures has fundamentally transformed the field of natural language processing. These models, characterized by their autoregressive generation capabilities and unidirectional attention mechanisms, have demonstrated unprecedented performance across a wide range of language understanding and generation tasks.

The success of models such as GPT-3 (Brown et al., 2020), LLaMA (Touvron et al., 2023), and PaLM (Chowdhery et al., 2022) has established decoder-only architectures as the dominant paradigm for large-scale language modeling. This paper provides a systematic examination of the theoretical foundations, architectural components, and practical considerations underlying these models.

2. Theoretical Foundations

2.1 Autoregressive Language Modeling

Decoder-only models are fundamentally based on the principle of autoregressive language modeling, where the probability of a sequence is decomposed as:

P(x₁, x₂, ..., xₙ) = ∏ᵢ₌₁ⁿ P(xᵢ | x₁, x₂, ..., xᵢ₋₁)

This decomposition enables the model to generate text sequentially, with each token conditioned on all previous tokens in the sequence.

2.2 Causal Attention Mechanism

The core innovation of decoder-only models lies in their use of causal (or masked) attention, which ensures that each position can only attend to previous positions in the sequence. This is implemented through a lower triangular attention mask that prevents information leakage from future tokens.

2.3 Self-Supervised Learning Objective

These models are trained using the next-token prediction objective, where the model learns to predict the next token in a sequence given the preceding context. This self-supervised approach eliminates the need for manually labeled training data.

3. Architectural Components

3.1 Input Processing Pipeline

The input processing pipeline consists of three primary stages:

Tokenization: Raw text is converted into discrete tokens using subword tokenization algorithms such as Byte Pair Encoding (BPE) or SentencePiece.

Embedding: Tokens are mapped to dense vector representations in a high-dimensional embedding space.

Positional Encoding: Position information is incorporated through either sinusoidal positional encodings or learned positional embeddings.

3.2 Multi-Head Self-Attention

The attention mechanism is the core computational component, enabling the model to focus on relevant parts of the input sequence. The scaled dot-product attention is computed as:

Attention(Q, K, V) = softmax(QKᵀ/√dₖ)V

where Q, K, and V represent query, key, and value matrices respectively, and dₖ is the dimension of the key vectors.

3.3 Feed-Forward Networks

Each attention layer is followed by a position-wise feed-forward network that applies the same transformation to each position independently:

FFN(x) = max(0, xW₁ + b₁)W₂ + b₂

3.4 Layer Normalization and Residual Connections

Layer normalization is applied to stabilize training, while residual connections facilitate gradient flow during backpropagation. The choice between pre-norm and post-norm configurations significantly impacts training dynamics.

4. Training Methodology

4.1 Data Preparation and Preprocessing

Training data undergoes extensive preprocessing including:

  • Text cleaning and normalization
  • Deduplication to prevent data leakage
  • Quality filtering based on various heuristics
  • Chunking into fixed-length sequences

4.2 Optimization Strategies

Learning Rate Scheduling: Cosine annealing with warmup is commonly employed to stabilize early training phases.

Gradient Accumulation: Enables effective batch sizes larger than what fits in memory.

Mixed Precision Training: Reduces memory requirements while maintaining numerical stability.

4.3 Regularization Techniques

  • Dropout applied to attention weights and feed-forward layers
  • Weight decay for parameter regularization
  • Gradient clipping to prevent exploding gradients

5. Scaling Considerations

5.1 Model Scaling Laws

Empirical studies have identified power-law relationships between model size, dataset size, and computational resources. The scaling laws suggest that performance improves predictably with increased model parameters, training data, and compute.

5.2 Infrastructure Requirements

Training large-scale models requires:

  • Distributed training across multiple accelerators
  • Model parallelism for parameters exceeding single-device memory
  • Pipeline parallelism for very deep networks
  • Efficient communication protocols for gradient synchronization

5.3 Memory Optimization

Several techniques have been developed to manage memory requirements:

  • Gradient checkpointing trades computation for memory
  • Activation checkpointing recomputes intermediate activations
  • ZeRO optimizer partitions optimizer states across devices

6. Architectural Variants and Evolution

6.1 GPT Family Evolution

The GPT series demonstrates the evolution of decoder-only architectures:

  • GPT-1: 117M parameters, 12 layers, established the foundation
  • GPT-2: 1.5B parameters, 48 layers, introduced scaling principles
  • GPT-3: 175B parameters, 96 layers, demonstrated few-shot learning
  • GPT-4: Estimated 1.7T parameters, represents current state-of-the-art

6.2 LLaMA Architecture

Meta's LLaMA models introduced several innovations:

  • RMSNorm instead of LayerNorm
  • SwiGLU activation functions
  • Rotary Position Embeddings (RoPE)
  • Efficient attention implementations

6.3 PaLM and Scaling Insights

Google's PaLM models (up to 540B parameters) provided insights into:

  • The importance of data quality over quantity
  • The role of instruction tuning
  • Emergent capabilities at scale

7. Training Challenges and Solutions

7.1 Computational Challenges

Training large models presents several computational challenges:

  • Memory Requirements: Models with billions of parameters require significant memory
  • Training Time: Weeks to months of continuous training
  • Energy Consumption: Substantial carbon footprint
  • Hardware Costs: Millions of dollars in compute resources

7.2 Data Quality and Bias

Ensuring high-quality training data is crucial:

  • Bias Mitigation: Addressing demographic and cultural biases
  • Data Diversity: Ensuring representative coverage of languages and domains
  • Quality Filtering: Removing low-quality or harmful content
  • Deduplication: Preventing data leakage and overfitting

7.3 Evaluation and Benchmarking

Developing robust evaluation methodologies:

  • Perplexity: Standard metric for language modeling
  • Downstream Tasks: Performance on specific NLP benchmarks
  • Human Evaluation: Subjective assessment of generation quality
  • Bias Testing: Systematic evaluation of fairness across demographics

8. Best Practices and Recommendations

8.1 Architecture Design Principles

  • Depth vs. Width: Balance between model depth and width based on computational constraints
  • Attention Head Configuration: Typically 8-16 heads provide good performance
  • Feed-Forward Ratio: 4x the hidden dimension is a common choice
  • Activation Functions: GELU and SwiGLU are preferred over ReLU

8.2 Training Strategy

  • Learning Rate: Start with 1e-4, use cosine decay
  • Batch Size: Maximize within memory constraints
  • Warmup Period: Gradual learning rate increase for stability
  • Regularization: Appropriate dropout and weight decay

8.3 Data Strategy

  • Quality over Quantity: Prioritize high-quality data sources
  • Diverse Sources: Include books, web text, code, and other domains
  • Balanced Sampling: Avoid over-representing common patterns
  • Continuous Evaluation: Monitor training metrics and adjust accordingly

9. Future Directions and Research Opportunities

9.1 Efficiency Improvements

  • Sparse Attention: Reduce computational complexity through attention sparsity
  • Mixture of Experts: Conditional computation for different inputs
  • Quantization: Reduce precision requirements without significant performance loss
  • Pruning: Remove unnecessary parameters while maintaining performance

9.2 Architectural Innovations

  • Retrieval-Augmented Generation: Incorporate external knowledge sources
  • Multimodal Capabilities: Extend to vision, audio, and other modalities
  • Longer Contexts: Handle sequences of arbitrary length
  • Improved Few-Shot Learning: Better in-context learning capabilities

9.3 Training Methodologies

  • Continual Learning: Update models without catastrophic forgetting
  • Federated Learning: Train on distributed data sources
  • Few-Shot Adaptation: Rapid adaptation to new tasks
  • Unsupervised Learning: Reduce reliance on labeled data

10. Conclusion

Decoder-only transformer architectures have established themselves as the dominant paradigm for large-scale language modeling. The combination of autoregressive generation, causal attention mechanisms, and self-supervised learning has enabled the development of models with unprecedented capabilities.

The success of these architectures demonstrates the importance of:

  • Scalable Architectures: Designs that can effectively utilize increased computational resources
  • Quality Data: The critical role of high-quality, diverse training data
  • Efficient Training: Optimization strategies that enable training of very large models
  • Robust Evaluation: Comprehensive assessment methodologies

As we look toward the future, the continued evolution of decoder-only models will likely focus on efficiency improvements, multimodal capabilities, and more sophisticated reasoning abilities. The insights gained from studying these architectures provide a foundation for the next generation of language models.

References

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.

Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., ... & Fiedel, N. (2022). Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311.

Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M. A., Lacroix, T., ... & Lample, G. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.

This is the first part of a comprehensive series on Large Language Model architectures. The next installment will examine encoder-decoder models and their applications in sequence-to-sequence tasks.