Multimodal model quantization support through LLM Compressor

The blog post discusses version 0.4.0 of LLM Compressor, which now supports multimodal model quantization, allowing for efficient compression of vision-language and audio models using the GPTQ algorithm. With high recoverability rates exceeding 99% and reduced memory and compute requirements, the release aims to enhance the deployment of AI models. It explains various compression techniques, outlines examples for quantization, and provides a framework for developers to improve model efficiency and scalability. Integration with vLLM is emphasized for better performance in real-world applications.