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. A key innovation is its advanced dataset generation method, which accurately detects and separates images and their corresponding textual descriptions from PDF documents like scientific papers. This 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 science-focused Wikipedia pages, enabling it to interpret complex visual scenes, generate precise language descriptions, and answer image-related queries. The model combines a vision encoder with an autoregressive transformer, supporting complex natural language understanding and enabling image-to-text-to-image or image-to-text-to-3D pipelines. It explores larger models through mixture-of-expert models and model merging, combining layers from different pre-trained models to leverage domain-specific expertise and general conversational capabilities. Model weights for sizes 4B to 12B parameters are provided for various applications. Cephalo is applied in bio-inspired design, mechanical properties, and materials science, analyzing materials phenomena like failure and fracture, microstructures, and reasoning over biological and synthetic materials. It can predict statistical features of stress and atomic energy distributions, crack dynamics, and damage in materials. The model's ability to understand complex physical and mechanical behaviors enables the design of more resilient and high-performance materials. Cephalo's architecture allows for tightly coupled visual and linguistic data processing, with various model sizes explored for research-focused applications. The model's performance is evaluated in diverse use cases, including fracture mechanics, protein mechanics, bio-inspired AI systems, and bio-inspired materials. Cephalo's integration of image and text data enables it to generate detailed descriptions, make quantitative predictions, and develop bio-inspired design concepts. The model's ability to reason over complex data and generate accurate responses demonstrates its potential in materials science and interdisciplinary research. Cephalo's application in bio-inspired materials accelerates research through automated literature reviews and data extraction, enhances understanding of natural materials, and supports the design of innovative bio-inspired solutions. The model's performance is evaluated in various experiments, showing its capability to analyze images, generate text, and develop design concepts. The model's integration of image and text data enables it to generate detailed descriptions, make quantitative predictions, and develop bio-inspired design concepts. Cephalo's ability to reason over complex data and generate accurate responses demonstrates its potential in materials science and interdisciplinary research.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. A key innovation is its advanced dataset generation method, which accurately detects and separates images and their corresponding textual descriptions from PDF documents like scientific papers. This 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 science-focused Wikipedia pages, enabling it to interpret complex visual scenes, generate precise language descriptions, and answer image-related queries. The model combines a vision encoder with an autoregressive transformer, supporting complex natural language understanding and enabling image-to-text-to-image or image-to-text-to-3D pipelines. It explores larger models through mixture-of-expert models and model merging, combining layers from different pre-trained models to leverage domain-specific expertise and general conversational capabilities. Model weights for sizes 4B to 12B parameters are provided for various applications. Cephalo is applied in bio-inspired design, mechanical properties, and materials science, analyzing materials phenomena like failure and fracture, microstructures, and reasoning over biological and synthetic materials. It can predict statistical features of stress and atomic energy distributions, crack dynamics, and damage in materials. The model's ability to understand complex physical and mechanical behaviors enables the design of more resilient and high-performance materials. Cephalo's architecture allows for tightly coupled visual and linguistic data processing, with various model sizes explored for research-focused applications. The model's performance is evaluated in diverse use cases, including fracture mechanics, protein mechanics, bio-inspired AI systems, and bio-inspired materials. Cephalo's integration of image and text data enables it to generate detailed descriptions, make quantitative predictions, and develop bio-inspired design concepts. The model's ability to reason over complex data and generate accurate responses demonstrates its potential in materials science and interdisciplinary research. Cephalo's application in bio-inspired materials accelerates research through automated literature reviews and data extraction, enhances understanding of natural materials, and supports the design of innovative bio-inspired solutions. The model's performance is evaluated in various experiments, showing its capability to analyze images, generate text, and develop design concepts. The model's integration of image and text data enables it to generate detailed descriptions, make quantitative predictions, and develop bio-inspired design concepts. Cephalo's ability to reason over complex data and generate accurate responses demonstrates its potential in materials science and interdisciplinary research.