Cephalo is a series of multimodal vision-large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data to enhance understanding and interaction within human-AI and multi-agent AI frameworks. The key innovation of Cephalo is its advanced dataset generation method, which accurately detects and separates images and their corresponding textual descriptions from scientific papers. The method refines image-text pairs through integrated vision and language processing, ensuring high-quality, contextually relevant training data. Cephalo is trained on integrated image and text data from thousands of scientific papers and Wikipedia pages, enabling it to interpret complex visual scenes, generate precise language descriptions, and answer queries about images effectively. The model combines a vision encoder with an autoregressive transformer, supporting complex natural language understanding and the creation of image-to-text-to-image or image-to-text-to-3D pipelines. To develop larger models, Cephalo explores mixture-of-expert models and model merging, combining layers from different pre-trained models to leverage domain-specific expertise and general conversational capabilities. Various model sizes, ranging from 4 billion to 12 billion parameters, are provided to accommodate different computational needs and applications. Cephalo is evaluated in diverse use cases, including biological materials, fracture and engineering analysis, protein biophysics, and bio-inspired design. The model demonstrates enhanced capabilities in predicting statistical features of stress and atomic energy distributions, as well as crack dynamics and damage in materials. The paper discusses challenges and opportunities, providing a detailed overview of the development and application of Cephalo in materials science.Cephalo is a series of multimodal vision-large language models (V-LLMs) designed for materials science applications, integrating visual and linguistic data to enhance understanding and interaction within human-AI and multi-agent AI frameworks. The key innovation of Cephalo is its advanced dataset generation method, which accurately detects and separates images and their corresponding textual descriptions from scientific papers. The method refines image-text pairs through integrated vision and language processing, ensuring high-quality, contextually relevant training data. Cephalo is trained on integrated image and text data from thousands of scientific papers and Wikipedia pages, enabling it to interpret complex visual scenes, generate precise language descriptions, and answer queries about images effectively. The model combines a vision encoder with an autoregressive transformer, supporting complex natural language understanding and the creation of image-to-text-to-image or image-to-text-to-3D pipelines. To develop larger models, Cephalo explores mixture-of-expert models and model merging, combining layers from different pre-trained models to leverage domain-specific expertise and general conversational capabilities. Various model sizes, ranging from 4 billion to 12 billion parameters, are provided to accommodate different computational needs and applications. Cephalo is evaluated in diverse use cases, including biological materials, fracture and engineering analysis, protein biophysics, and bio-inspired design. The model demonstrates enhanced capabilities in predicting statistical features of stress and atomic energy distributions, as well as crack dynamics and damage in materials. The paper discusses challenges and opportunities, providing a detailed overview of the development and application of Cephalo in materials science.