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README.md
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The image shows a summary of model merging approach, constructing larger models from smaller pre-trained building blocks. a, Fine-tuning the base model. b, Constructing the larger, merged model by combining the whole or parts of smaller models. c, Fine-tuning the integrated hybrid, merged, model.
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### Additional codes and tools
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Additional codes and tools are provided at [https://github.com/lamm-mit/Cephalo](https://github.com/lamm-mit/Cephalo).
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The image shows a summary of model merging approach, constructing larger models from smaller pre-trained building blocks. a, Fine-tuning the base model. b, Constructing the larger, merged model by combining the whole or parts of smaller models. c, Fine-tuning the integrated hybrid, merged, model.
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## Etymology and inspiration behind the name "``"Cephalo"
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The name "Cephalo" is derived from the Greek word κεφαλή, or kephalē, meaning ``head" or ``brain," which symbolizes the model's central role in processing and integrating visual and linguistic information. This name reflects the model's function as the ``brain" of the system, facilitating advanced human-AI and multi-agent AI interactions through the comprehensive understanding of multimodal data.
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Additionally, ``Cephalo" draws inspiration from cephalopods, a class of intelligent mollusks that includes octopuses, squids, and cuttlefish, associating it with the focus on biological inspiration that is central to the training and use of the model. Cephalopods are renowned for their exceptional cognitive abilities, advanced problem-solving skills, and highly developed nervous systems. They exhibit remarkable adaptability to their environments, sophisticated camouflage techniques, and complex behaviors, and are well-equipment to integrate visual cues with materialization.
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By naming our multimodal materials science V-LLM ``Cephalo," we evoke the intelligence and adaptability of cephalopods. Similar to how cephalopods process diverse sensory inputs to navigate and respond to their surroundings, Cephalo integrates and processes visual and linguistic data to handle complex tasks. This dual inspiration highlights the model's potential for advanced problem-solving and contextual comprehension, drawing parallels between the cognitive prowess of cephalopods and the model's capabilities in the realm of materials science and beyond.
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### Additional codes and tools
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Additional codes and tools are provided at [https://github.com/lamm-mit/Cephalo](https://github.com/lamm-mit/Cephalo).
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