TensorRT-LLM has long been a critical tool for optimizing inference in models such as decoder-only architectures like Llama 3.1, mixture-of-experts models like Mixtral, and selective state-space ...

Additionally, MSEED incorporates a simple vanilla encoder-decoder model for strengthening rolling predictions. The framework has been tested on four challenging real-world datasets, focusing on two ...

I am not completely sure from the documentation how to interpret the results of the TFTExplainer. When I pass a certain series to the explainer and plot variable selection, I get a graph of variable ...

Decoder-based LLMs can be broadly classified into three main types: encoder-decoder, causal decoder, and prefix decoder. Each architecture type exhibits distinct attention patterns. Encoder-Decoder ...

In order to overcome the drawback of decoder-only LLMs for text embedding, a team of researchers from Mila, McGill University, ServiceNow Research, and Facebook CIFAR AI Chair has proposed LLM2Vec, a ...

A simple sentencepiece encoder and decoder. Note: This is not a new sentencepiece toolkit, it just uses google's sentencepiece model as input and encode the string to ids/pieces or decode the ids to ...