Unverified50% confidenceFactExact time
Model merging operates directly in parameter space, combining the weights of multiple models in some manner; it requires no training data or gradient backpropagation, and can produce a new model through purely algebraic operations on the weights.
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10/7/2026
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VerifiedFine-tuning involves taking an existing large model and further training it with a specially prepared proprietary dataset, thereby changing the model's weight values79% similarVerifiedModel Merging can fuse capability characteristics of different versions without retraining by performing linear interpolation or spherical interpolation (SLERP) on the weights of different versions in parameter space.79% similarUnverifiedOrchestration frameworks extend the practical boundaries of models by building a perception-memory-action enhancement layer outside the model, without retraining the model.77% similarUnverifiedA base model is the original version that has completed pre-training on large-scale data but has not been fine-tuned for a specific task, retaining the most complete parameter space and generalization potential.77% similarUnverifiedMerging the parameters of multiple trained models to generate new trade-off combinations has been shown to be helpful.76% similar
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