Building blocks of LLM | Slides

Summary: The article discusses the key components of training a large language model, emphasizing the importance of quality data, model architecture, training process, inference capabilities, and parameter optimization for achieving human-like text generation.
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Building Block Description
Data The data used to train a large language model is crucial for its performance. This includes a vast amount of text data from various sources such as books, articles, websites, and more. The quality and diversity of the data directly impact the model's ability to understand and generate human-like text.
Model Architecture The architecture of a large language model refers to the structure and design of the neural network used for processing and generating text. Common architectures include Transformer-based models like GPT (Generative Pre-trained Transformer) which consist of multiple layers of attention mechanisms.
Training Training a large language model involves feeding the model with the data and optimizing its parameters to minimize the loss function. This process requires significant computational resources and time, often utilizing powerful GPUs or TPUs to handle the massive amount of data and complex model architecture.
Inference Once trained, the large language model can be used for inference, which involves generating text based on input prompts or completing sentences. During inference, the model utilizes its learned parameters to predict the most likely next words or sequences, producing coherent and contextually relevant text.
Parameters The parameters of a large language model refer to the weights and biases learned during training that define the model's behavior. These parameters are fine-tuned through optimization algorithms such as gradient descent to improve the model's ability to understand and generate natural language.

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