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model-merging

Merge fine-tuned models using linear, SLERP, task arithmetic, TIES, and DARE methods.

Updated Mar 18, 2026
Search Tags:#merge#mergekit#transformers#slerp#model-merging#task-arithmetic#fine-tuned
One-click install
npx skills add https://github.com/tadod12/fraud-detection-research --skill model-merging-tadod12
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-merging
Source: https://github.com/tadod12/fraud-detection-research/tree/main/.agent/skills/19-emerging-techniques/model-merging
Command: npx skills add https://github.com/tadod12/fraud-detection-research --skill model-merging-tadod12

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Merges multiple fine-tuned models to create a single, enhanced merged model.

Core Features & Use Cases

  • Supports Linear (Model Soup), SLERP, Task Arithmetic, TIES, and DARE merging methods.
  • Handles multiple sources with a base architecture constraint; can produce MoE-style ensembles and tokenizer merges; supports production deployment workflows.
  • Real-world scenario: combine math, code, and chat specialization into a single merged model for versatile tasks.

Quick Start

Provide a base model, one or more fine-tuned models, and a merge configuration, then run the merge to produce a new merged model.

Frequently Asked Questions about model-merging

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I merge multiple fine-tuned models without retraining?▼

To merge fine-tuned models without retraining, provide a shared base model and one or more fine-tuned variants with a merge configuration. The Skill uses mergekit to combine them into a single enhanced model.

What is the difference between SLERP, TIES, and DARE model merging?▼

SLERP interpolates model weights via spherical interpolation, TIES resolves parameter conflicts by trimming and signing deltas, and DARE randomly drops delta weights to reduce interference during model merging.

Can I combine math, code, and chat fine-tuned models into one assistant?▼

Yes, you can combine math, code, and chat fine-tuned models into a single versatile assistant. The Skill merges multiple specialized models sharing the same base architecture into one enhanced model.

Do I need models with the same base architecture to use task arithmetic merging?▼

Yes, task arithmetic merging requires models with the same base architecture. The Skill enforces this constraint to safely apply task vectors and merge fine-tuned models using mergekit tooling.

Can I merge tokenizers and produce deployment-ready models with mergekit?▼

Yes, the Skill optionally merges tokenizers alongside model weights and produces deployment-ready artifacts. This supports production workflows for generating ready-to-use merged models.

What are the limitations of using model merging for MoE-style ensembles?▼

A limitation of model merging for MoE-style ensembles is the strict requirement for a shared base architecture. Models with different architectures cannot be directly merged using linear, SLERP, or TIES methods.

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