The decoder-only transformer architecture is comprised of several “blocks” with identical structure that are stacked in sequence. Within each of these blocks, there are two primary components: Masked, multi-headed self-attention.

Feb 7, 2024 · In this work, we propose StableMask: a parameter-free method to address both limitations by refining the causal mask. It introduces pseudo-attention values to balance attention distributions and encodes absolute positional information via a …

May 30, 2023 · LLMs/GPT models use a variant of this architecture called de' decoder-only transformer'. The most popular variety of transformers are currently these GPT models. The only purpose of these models is to receive a prompt (an input) and predict the next token/word that comes after this input.

Sep 20, 2019 · Transformer architecture was introduced as a novel pure attention-only sequence-to-sequence architecture by Vaswani et al. Its ability for parallelizable training and its general performance improvement made it a popular option among NLP (and recently CV) researchers.

Feb 22, 2024 · In general, encoder-only architectures are proficient in extracting information from text for tasks like classification and regression, whereas decoder-only models specialize in generating text. For instance, GPT, which focuses on text generation, falls under the category of decoder-only models.

Aug 31, 2024 · Large language models (LLMs) like GPT-3, LLaMA, and Gemini are revolutionizing how we interact with and generate text. At the heart of these powerful models lies a specific type of neural network...

In this work, we propose StableMask: a parameter-free method to address both limitations by refining the causal mask. It introduces pseudo-attention values to balance attention distributions and encodes abso-lute positional information via a progressively de-creasing mask ratio.

Dec 20, 2024 · Most popular examples of AI models based on the decoder-only transformer are: GPT, PaLM, LaMDa, Llama, and Falcon [23, 24]. This paper will explain about the transformer and its architectural components and working.

Feb 16, 2025 · Rather than feeding the embedded word vector (i.e., token embedding plus positional encoding) directly into the decoder layers, the Transformer first computes the Query (Q), Key (K), and Value (V) vectors from the word vector. These vectors are then used in the self-attention mechanism.

In order to build a decoder-only transformer in TensorFlow, we need to implement the components of the decoder, such as self-attention layers, feed-forward networks, and positional encodings. Here’s an implementation example of a decoder-only transformer in TensorFlow: