LLMs
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A Transformer is a neural network architecture that relies on self-attention mechanisms to process input data, enabling parallel processing and capturing long-range dependencies. It has revolutionized natural language processing and is now widely used in computer vision and other fields.
Explanation
Transformers eschew recurrent layers, instead using self-attention to weigh the importance of different parts of the input sequence when processing each element. This allows for parallel computation, a significant speedup compared to recurrent models. The original Transformer architecture consists of an encoder and a decoder. The encoder processes the input sequence and creates a contextualized representation. The decoder then uses this representation to generate the output sequence. Key components include multi-head attention (allowing the model to attend to different aspects of the input), positional encodings (to provide information about the position of tokens), and residual connections with layer normalization (to improve training stability). Transformers are pre-trained on massive datasets and then fine-tuned for specific tasks. Their ability to capture complex relationships and dependencies in data has led to breakthroughs in various AI domains.