A large language model (LLM) is a very large neural language model trained on broad text corpora to predict or generate language. It can produce code, mathematical expressions, structured data, and natural-language content, but its usefulness depends on accuracy, compute cost, speed, data governance, and deployment context. section 1.0 section 1.7
Key operational distinctions include:
- Transformers process complete text sequences through self-attention; GPT models use decoder components for generation, while BERT uses encoder components for classification and prediction. section 1.7
- Generalist models offer broad capabilities but can expose data or obscure training and architecture details; domain-specific models can improve specialized accuracy, compliance, and control. section 1.0 section 1.7
- Open models provide pretrained starting points, while transfer learning and fine-tuning adapt them to organizational or domain data at lower cost than training from scratch. section 1.7
Inference reference
GPT-Neo large demonstrates practical LLM inference: its 2.7 billion trainable parameters and Transformer architecture make execution speed, accuracy, memory, and compute cost visible concerns when using Hugging Face Transformers, Accelerate, and quantization methods. section 4.1
