This paper introduces a novel graphical model architecture for robust and vocabulary independent keyword spotting which does not require the training of an explicit garbage model. We show how a ...

We present the results of experiments on minimizing the model size for the text-based Open Vocabulary Keyword Spotting task. The main goal is to perform inference on devices with limited computing ...

The AI research community continues to find new ways to improve large language models (LLMs), the latest being a new architecture introduced by scientists at Meta and the University of Washington.

Current state-of-the-art models for natural language understanding require a preprocessing step to convert raw text into discrete tokens. This process known as tokenization relies on a pre-built ...

Cross-Architecture Adaptation: Using mergekit-tokensurgeon, a version of Qwen2.5-14B was created that uses the vocabulary of Llama 3.1 405B. This allowed for the use of Llama 3.1 405B logits in ...

The model comprises two main parts: a pre-trained speech model based on transformer architecture to extract features (embedding vectors), named HuBERT, and accepts a float array corresponding to the ...

These LVLMs typically employ two main structures: image tokens as prefixes or cross-attention for feature fusion. However, regardless of architecture, the model’s upper limit may be constrained by the ...