Weight Merging

Definitions

1. Combining weights or learned parameter changes from multiple models, adapters, or fine-tunes to produce a merged model. Weight merging is explored as a way to combine capabilities without full retraining.

   In plain English: Combining weights from multiple model variants into one model.

   Example: "They tried weight merging to blend domain-specific expertise from two separate fine-tunes into one deployment target."

Related Terms

• AI Model Registry
• Attention Score
• Bidirectional Encoder
• Consistency Model
• Convolutional Neural Network
• Decoder
• OpenAI Codex
• Model Weight
• Surrogate Model
• Weight Quantization