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A new evolutionary technique from Japan-based AI lab Sakana AI enables developers to augment the capabilities of AI models without costly training and fine-tuning processes. The technique, called Model Merging of Natural Niches (M2N2), overcomes the limitations of other model merging methods and can even evolve new models entirely from scratch.
M2N2 can be applied to different types of machine learning models, including large language models (LLMs) and text-to-image generators. For enterprises looking to build custom AI solutions, the approach offers a powerful and efficient way to create specialized models by combining the strengths of existing open-source variants.
What is model merging?
Model merging is a technique for integrating the knowledge of multiple specialized AI models into a single, more capable model. Instead of fine-tuning, which refines a single pre-trained model using new data, merging combines the parameters of several models simultaneously. This process can consolidate a wealth of knowledge into one asset without requiring expensive, gradient-based training or access to the original training data.
