# Model Merging

Merge multiple fine-tuned models using mergekit to combine capabilities without...

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Model Merging: Combining Pre-trained Models

## When to Use This Skill

Use Model Merging when you need to:
- **Combine capabilities** from multiple fine-tuned models without retraining
- **Create specialized models** by blending domain-specific expertise (math + coding + chat)
- **Improve performance** beyond single models (often +5-10% on benchmarks)
- **Reduce training costs** - no GPUs needed, merges run on CPU
- **Experiment rapidly** - create new model variants in minutes, not days
- **Preserve multiple skills** - merge without catastrophic forgetting

**Success Stories**: Marcoro14-7B-slerp (best on Open LLM Leaderboard 02/2024), many top HuggingFace models use merging

**Tools**: mergekit (Arcee AI), LazyMergekit, Model Soup

## Installation

```bash
# Install mergekit
git clone https://github.com/arcee-ai/mergekit.git
cd mergekit
pip install -e .

# Or via pip
pip install mergekit

# Optional: Transformer library
pip install transformers torch
```

## Quick Start

### Simple Linear Merge

```yaml
# config.yml - Merge two models with equal weights
merge_method: linear
models:
  - model: mistralai/Mistral-7B-v0.1
    parameters:
      weight: 0.5
  - model: teknium/OpenHermes-2.5-Mistral-7B
    parameters:
      weight: 0.5
dtype: bfloat16
```

```bash
# Run merge
mergekit-yaml config.yml ./merged-model --cuda

# Use merged model
python -m transformers.models.auto --model_name_or_path ./merged-model
```

### SLERP Merge (Best for 2 Models)

```yaml
# config.yml - Spherical interpolation
merge_method: slerp
slices:
  - sources:
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [0, 32]
      - model: teknium/OpenHermes-2.5-Mistral-7B
        layer_range: [0, 32]
parameters:
  t: 0.5  # Interpolation factor (0=model1, 1=model2)
dtype: bfloat16
```

## Core Concepts

### 1. Merge Methods

**Linear (Model Soup)**
- Simple weighted average of parameters
- Fast, works well for similar models
- Can merge 2+ models (`w1 + w2 + ... = 1`)

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/19-emerging-techniques/model-merging/SKILL.md