In this final part of my GPT model implementation, I focus on integrating model introspection to better understand the architecture and parameters. To monitor training, I visualized the loss using both simple and exponential moving averages, smoothing the trends and making it easier to evaluate performance.
In this post, I outline the process of enhancing my GPT model by stacking multiple attention blocks, significantly improving the model's ability to capture complex patterns in text sequences. Each block consists of a multi-head attention layer and a feedforward neural network, allowing the model to refine its understanding of token relationships through progressively deeper representations. After training for 500 epochs, I demonstrate the improvement in phrase structure and coherence in the generated output. The method I describe offers valuable insights for anyone interested in building advanced models for natural language processing task.
In this post, I walk through the process of implementing both single-head and multi-head attention mechanisms for my GPT model. Starting with the basics of attention, I describe how I build the single-head version and then extend it to handle multiple attention heads. Each step involves clear and concise Python code, emphasizing how the model handles queries, keys, and values to compute attention scores. I then explain how to switch between the two attention mechanisms, maintaining flexibility in my model's architecture. This post serves as a practical guide for implementing attention mechanisms in transformers.
In this post, I detail the recent enhancements I made to my GPT model. After upgrading the tokenizer by integrating "tiktoken" for better handling of input, I implemented a positional encoding (PE) layer, a crucial step to improve the model's ability to process sequential data. The use of sinusoidal PE helps the model understand the relative position of tokens, a vital improvement for any transformer-based architecture. Additionally, I included a simple feedforward neural network layer to refine the output before moving on to adding the attention mechanism, setting the stage for further advancements.
In this post, I discuss the decoding process of a simple GPT model that I trained for 150 epochs. After analyzing the training results, I demonstrate how I generate text based on an input sequence using tokenization and sampling methods such as top-k sampling and temperature scaling. I explain how the model predicts the next token in the sequence and how the process iteratively continues. Despite the limitations of my basic model, this article serves as a basis for future improvements, especially as I explore adding attention mechanisms to enhance performance.
In my latest post, I explore how to build a GPT model from scratch using PyTorch. I explain the initialization of the model with embedding layers, layer normalization, and linear output layers, followed by the step-by-step training process using cross-entropy loss and the Adam optimizer. The focus is on how each token in the sequence is converted into a dense vector and how the model is trained to predict the next token. I also discuss the importance of saving the model's state for future inference or fine-tuning, ensuring efficient use in real-time applications.
In this blog post, I present the first steps in building a GPT model using PyTorch, focusing on creating a tokenizer and an efficient data loader. The tokenizer converts raw text into numerical representations, facilitating the processing of large textual datasets. I use Divina Commedia by Dante Alighieri as the text corpus, explaining how tokenization works, how vocabulary mapping is done, and how the tokenized data is transformed into PyTorch tensors. Additionally, I implement a data loader that splits the dataset into training and evaluation sets, ensuring efficient batching and iteration during the training process.
In my exploration of large language models (LLMs), I focus on addressing their limitations, such as reliance on static data, hallucination, and context retention issues. LLMs, while powerful, often struggle with generating factually accurate responses and retaining context over long conversations. My work highlights recent technical advancements, such as retrieval-augmented generation (RAG) and fine-tuning, which allow for more accurate, scalable, and context-aware outputs. By incorporating real-time data retrieval and domain-specific fine-tuning, I show how LLMs can better meet the needs of specialized fields.
In my exploration of large language models (LLMs), I focus on addressing their limitations, such as reliance on static data, hallucination, and context retention issues. LLMs, while powerful, often struggle with generating factually accurate responses and retaining context over long conversations. My work highlights recent technical advancements, such as retrieval-augmented generation (RAG) and fine-tuning, which allow for more accurate, scalable, and context-aware outputs. By incorporating real-time data retrieval and domain-specific fine-tuning, I show how LLMs can better meet the needs of specialized fields.
The attention mechanism is the core innovation of the Transformer architecture, transforming how sequential data is processed in natural language processing. Unlike traditional models, which handle sequences one step at a time, the attention mechanism enables models to consider all positions in the input at once. This allows each word to relate to every other word, regardless of position, improving the capture of dependencies over long distances. Through attention weights, the model determines how much focus each word should have in context. This mechanism is key in language models like GPT and BERT, revolutionizing tasks like machine translation and text generation.
In this post, I explore the architecture of Transformer networks, which have become a fundamental component in natural language processing tasks like machine translation and text generation. The use of attention mechanisms enables efficient parallel processing of input data, addressing long-range dependencies in sequences better than traditional recurrent networks. I focus on the main components, the embedding layer, encoder, decoder, and output layer, while emphasizing how these elements work together to predict tokens and capture contextual information. Understanding these principles is crucial for grasping the design of advanced models like GPT and BERT.
In my latest post, I explore the mechanics of Coulomb friction, focusing on how it affects motion between a body and a surface. I explain the distinction between static and kinetic friction, describing the force thresholds required to initiate movement and the constant resistance during sliding. Additionally, I cover tipping scenarios, where instead of slipping, a body might rotate around a contact edge due to an applied force. This creates a situation where the normal force shifts and the system reaches an impending tip condition.
In this article, I explore the differences between the catenary and parabolic shapes of cables under various load conditions. A cable subjected to its own weight forms a catenary, commonly seen in suspension bridges and power lines. When the same cable experiences a uniform distributed load, it assumes a parabolic shape, as observed in suspension bridge roadbeds. By understanding these curves, I provide insights into their applications in structural engineering.
In this post, I explore the fundamental principles of cable systems, focusing on how they operate under various loading conditions. Cables, which only resist tension, are incredibly efficient in spanning large distances with minimal material. I highlight examples like power lines, suspension bridges, and ski lifts to demonstrate their versatility. Whether it's handling self-weight, concentrated loads, or distributed loads, cables adapt their shape to balance forces. I also discuss the advantages of using cable systems in construction, particularly for their flexibility, strength under tensile forces, and ability to create lightweight, visually striking designs.
In my exploration of cantilever beams subjected to external forces, I focus on how internal forces manifest as shear forces and bending moments. Shear forces act perpendicular to the beam's cross-section, causing internal sliding, while bending moments induce curvature. I also examine how the equilibrium is maintained by these forces at various sections of the beam. Additionally, I cover the relationship between distributed loads, shear forces, and bending moments, including the differential equations that govern them. These insights are crucial for understanding the behavior of beams under load, particularly in mechanical and structural applications.