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Sakana AI Introduces Evolutionary Model Merge: A New Machine Learning Approach Automating Foundation Model Development
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Evolutionary Model Merge: A Paradigm Shift in Foundation Model Development
A recent development of a model merging into the community of large language models (LLMs) presents a paradigm shift. Strategically combining multiple LLMs into a single architecture, this development approach has captivated the attention of researchers mainly due to the advantage that it requires no additional training, which cuts the cost of building new models significantly. This availability gave rise to interest and experimentation with model merging.
Prior efforts, such as the model soup approach, significantly improved relatively large image processing and classification models. Linear weight averaging works well for image processing and classification models and is also effective for image generation models such as latent diffusion models exemplified by Stable Diffusion.
Researchers from Sakana AI present a methodology that utilizes evolutionary algorithms to enhance the merging of foundation models. Their approach is distinguished by its ability to navigate both parameter space (weights) and the data flow space (inference path), a framework that integrates these two dimensions.
The researchers dissect the merging process into two distinct, orthogonal configuration spaces, analyzing their impacts. Building on this analysis, they introduced a cohesive framework that seamlessly integrates these spaces. They established merging configuration parameters for sparsification and weight mixing at each layer, including input and output embeddings.
The key contributions to the field of foundation model development made by the researchers’ work include:
- Automated Model Composition
- Cross-Domain Merging
- State-of-the-Art Performance
- High Efficiency and Surprising Generalizability
- Culturally-Aware VLM
In conclusion, the researchers from Sakana AI have proposed a general method that uses evolutionary techniques to efficiently discover the best ways to combine different models from the vast ocean of different open-source models with diverse capabilities. Their process can automatically create new foundation models with the desired capabilities specified by the user.
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Introducing Evolutionary Model Merge: A new approach bringing us closer to automating foundation model development. We use evolution to find great ways of combining open-source models, building new powerful foundation models with user-specified abilities! pic.twitter.com/msOokvqGbR
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