This is a quantized version of the Google Gemma-3n-E2B-it model, optimized by quantizing the weights to the INT4 data type, and can use the vLLM framework for efficient inference. The model supports audio-visual-text multimodal input and outputs text.

MultimodalTransformers

VideoLLaMA2.1-7B-AV is a multimodal large language model focused on audio-visual question answering tasks, capable of processing both video and audio inputs to provide high-quality question answering and description generation.

MultimodalTransformersEnglish

AV-HuBERT is an audio-visual speech recognition model trained on the MuAViC multilingual audio-visual corpus, combining audio and visual modalities for robust performance.

MultimodalTransformers

An audio-visual speech recognition model trained on the MuAViC dataset, which combines audio and visual modalities to improve recognition performance in noisy environments.

MultimodalTransformers

A multilingual audio-visual speech recognition model based on the MuAViC dataset, combining audio and visual modalities for robust performance

MultimodalTransformers