Positional encoding:Instead of looking at each word in the order that it appears in a sentence, a unique number is assigned to each word. This provides information about the position of each token (parts of the input such as words or subword pieces in NLP) in the sequence, allowing the m...
GPT-3.Generative Pre-trained Transformer (GPT) is a large language model for OpenAI'sChatGPT. The context window size forGPT-3is 2049 tokens. All GPT models are trained up to September 2021. GPT-3.5-turbo.GPT-3.5-turbo of OpenAI has a context window of 4,097 tokens. Another version, G...
Figure 1. How transformer models work. There are two key innovations that make transformers particularly adept for large language models: positional encodings and self-attention. Positional encoding embeds the order of which the input occurs within a given sequence. Essentially, instead of feeding word...
Transformer XL is a huge model hence it needs a high memory GPU setup to pre train or finetune. We will stick to just running inference in this article due to memory constraints huggingface provides this transformer model as a simple package.A sequence classification head is added on top of ...
Positional Encodings (PEs) are a critical component of Transformer-based Large Language Models (LLMs), providing the attention mechanism with important sequence-position information. One of the most popular types of encoding used today in LLMs are Rotary Positional Encodings (RoPE), that rotate the...
The first step in training a transformer model is to decompose the training text intotokens- in other words, identify each unique text value. For the sake of simplicity, you can think of each distinct word in the training text as a token (though in reality, tokens can be generated for pa...
The brain behind the magic is the neural network architecture, primarily built on transformer models. These transformers are game-changers, processing words in relation to one another instead of following a straight line. This fresh perspective allows them to grasp context and meaning with rem...
What is a transformer model? A transformer is a type of deep learning model that is widely used in NLP. Due to its task performance and scalability, it is the core of models like the GPT series (made by OpenAI), Claude (made by Anthropic), and Gemini (made by Google) and is extensi...
process data non-sequentially enables the decomposition of the complex problem into multiple, smaller, simultaneous computations. Naturally, GPUs are well suited to solve these types of problems in parallel, allowing for large-scale processing of large-scale unlabelled datasets and enormous transformer ...
Positional encoding is a representation of the order in which input words occur. A transformer is made up of multiple transformer blocks, also known as layers. For example, a transformer has self-attention layers, feed-forward layers, and normalization layers, all working together to decipher and...