An Architect's vision

  Encoder: Tokenize the input French phrase using the trained tokenizer. Add tokens to the input on the encoder side. Pass the tokens through the embedding layer. Feed tokens into the multi-headed attention layers. Outputs of the multi-headed attention layers are processed by a feed-forward network. The encoder output represents the structure and meaning of the input sequence. Decoder: Insert the encoder output into the middle of the decoder to influence self-attention mechanisms. Add a start-of-sequence token to the decoder input. The decoder uses contextual understanding from the encoder to predict the next token. The output of the decoder's self-attention layers is processed by the decoder feed-forward network. Pass the output through a final softmax output layer to get the first token. Continue the loop, passing the output token back to the input to predict the next token. Repeat until the model predicts an end-of-sequence token. The final sequence of tokens can be detokenized

  Large language models serve several significant purposes in the field of natural language processing (NLP) and artificial intelligence. Their capabilities have a broad range of applications and benefits: Natural Language Understanding: Large language models can understand and interpret human language at a deep level. They can extract meaning, sentiment, and context from text, making them valuable for sentiment analysis, language translation, and text summarization. Text Generation: These models are proficient at generating coherent and contextually relevant text. They can be used for content generation, chatbots, and creative writing tasks. Question Answering: Large language models can answer questions based on the information present in a given text. This is beneficial for chatbots, virtual assistants, and search engines. Language Translation: They are effective for translating text from one language to another, enabling cross-language communication and access to information. Se

  Aspect Encoding Decoding Primary Function Converts input data into a fixed-dimensional representation or embedding. Generates output data or sequences based on a given representation. Input Takes raw data, such as text, images, audio, or other forms. Receives a fixed-dimensional representation, often as a vector or tensor. Focus Learns to capture and abstract essential features or information from the input data. Transforms the fixed-dimensional representation into human-readable or interpretable output data. Direction Typically a forward process, moving from raw data to a compact representation. Usually a reverse process, taking a representation and producing data. Models Common models include Convolutional Neural Networks (CNNs) for images, Recurrent Neural Networks (RNNs) for sequential data, and Transformers for text. Examples include Recurrent decoders in sequence-to-sequence models and Language models like GPT (Generative Pre-trained Transformer). Use Cases Feature extraction,

Encoder-only, Decoder-only, and Encoder/Decoder models are used in various machine learning and natural language processing tasks. Here's where each type of model is typically applied, along with examples: Encoder-Only Model: Use: Encoder-only models are primarily used for tasks that involve feature extraction, representation learning, or encoding input data into a fixed-dimensional representation. Examples: Image Classification: Convolutional Neural Networks (CNNs) serve as encoder-only models to extract features from images before making classification decisions. Text Classification: Models like BERT (Bidirectional Encoder Representations from Transformers) encode text sequences into contextual embeddings, which are then used for various NLP tasks such as sentiment analysis or named entity recognition. Decoder-Only Model: Use: Decoder-only models are employed when the primary task is to generate structured or sequential output based on a fixed-dimensional representation. Exam