Mar 7, 2025 · We find that adjusting the model architecture to transfer model parameters from the decoder to the encoder results in a 3x inference speedup as measured by inverse real-time factor (RTFx) while preserving the accuracy and compute requirements for convergence.

greater flexibility across a range of applications. Specifically, we’ll introduce encoder-decoder networks, or sequence-to-sequence models, that are capable of generating contextually appropriate, arbitrary length, output sequences. Encoder-decoder net-works have been applied to a very wide range of applications including machine

Sep 28, 2024 · In decoder-only models, the prefill stage operates similarly to the encoder, while the decoding stage functions like the decoder in an encoder-decoder model. Understanding this breakdown will...

Many tutorials for seq2seq encoder-decoder architecture based on LSTM, (for example English-French translation), define the model as follow: # We discard `encoder_outputs` and only keep the states. # Set up the decoder, using `encoder_states` as initial state. initial_state=encoder_states) batch_size=128, epochs=100, validation_split=0.20)

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.

In contrast to DNNS, RNNs are capable of modeling a mapping to a variable number of target vectors. Let's dive a bit deeper into the functioning of RNN-based encoder-decoder models. During...

Encoder-decoder or sequence-to-sequence models are used for a different kind of sequence modeling in which the output sequence is a complex function of the entire input sequencer; we must map from a sequence of input words or tokens to a sequence of tags that are not merely direct mappings from individual words.

We will focus on the mathematical model defined by the architecture and how the model can be used in inference. Along the way, we will give some background on sequence-to-sequence models in NLP and break down the transformer-based encoder-decoder architecture into its encoder and decoder parts.

Encoder models use only the encoder of a Transformer model. At each stage, the attention layers can access all the words in the initial sentence. These models are often characterized as having “bi-directional” attention, and are often called auto-encoding models.

Mar 11, 2021 · In this article, I aim to explain the encoder-decoder sequence-to-sequence models in detail and help build your intuition behind its working. For this, I have taken a step-by-step...