Unlock the Potential of Vision-Language Models with vLLM
Experience the power of vision-language models with vLLM, the cutting-edge inference engine designed to support large language models (LLMs) with ease. With its versatile model architectures and quantization methods, vLLM is revolutionizing the way we interact with multimodal inputs.
Embark on a journey to explore the possibilities of vision-language models like Phi-3.5 Vision and Pixtral using vLLM. From image captioning to optical character recognition (OCR) and visual question answering (VQA), vLLM can cater to a wide array of tasks effortlessly.
In this detailed guide, delve into the intricacies of using vision-language models with vLLM. Uncover the key parameters that influence memory consumption and understand why VLMs demand more memory compared to standard LLMs. Get ready to analyze Phi-3.5 Vision and Pixtral as case studies in a multimodal application scenario involving text and images.
Ready to dive into the realm of Phi-3.5 Vision and Pixtral with vLLM? Access the notebook containing all the code snippets and instructions to kickstart your journey:
Embark on a thrilling journey into the world of transformer models where generating text becomes a seamless, efficient process. Join us as we explore the endless possibilities with vLLM and unlock the true potential of vision-language models!