Model Merging
Combining the weights of multiple fine-tuned models into a single model that inherits capabilities from all source models, without additional training.
Why It Matters
Model merging creates multi-talented models from single-talented ones — a coding model merged with a writing model produces one model good at both.
Example
Merging a Llama model fine-tuned on medical data with one fine-tuned on legal data, producing a single model that handles both domains.
Think of it like...
Like mixing paint colors — combining blue and yellow gives you green, and the merged model combines the strengths of its components.
Related Terms
Fine-Tuning
The process of taking a pre-trained model and further training it on a smaller, domain-specific dataset to specialize its behavior for a particular task or domain. Fine-tuning adjusts the model's weights to improve performance on the target task.
Model Weights
The collection of all learned parameter values in a neural network. Model weights are what you download when you get a pre-trained model — they encode everything the model learned.
Ensemble Learning
A strategy that combines multiple models to produce better predictions than any single model alone. Ensemble methods leverage the diversity of different models to reduce errors.
Parameter
Any learnable value in a machine learning model that is adjusted during training. Parameters include weights and biases in neural networks. Model size is often described by parameter count.