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Agent skill

Model Merging

by Orchestra-Research in Orchestra-Research/AI-Research-SKILLs

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

MITAuto-check passedAI & LLM Engineering

Install Model Merging

skills CLI
$ npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Orchestra-Research/AI-Research-SKILLs model-merging --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Orchestra-Research/AI-Research-SKILLs.git skills-src && mkdir -p .claude/skills && cp -r skills-src/19-emerging-techniques/model-merging .claude/skills/model-merging && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
model-merging
GitHub stars
13k
Used in
2 other repos
Token cost
~3.2k tokens
SKILL.md length
491 words
Files
5 (incl. references)
Skills in repo
96
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 7 steps: Merge Methods → Configuration Structure → Model Compatibility → …
  • Creating specialized models by blending domain-specific expertise (math + coding + chat)
  • SKILL.md covers When to Use This Skill, Installation, Quick Start and Core Concepts, plus 5 more sections
  • Calls pip, python and git; reaches github.com

What it does

Model Merging is an agent skill from Orchestra-Research/AI-Research-SKILLs. 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.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/coefficient-tuning.md`, `references/evaluation.md` and `references/examples.md`).

It sits in AI & LLM Engineering. The repository describes itself as: Comprehensive open-source library of AI research and engineering skills for any AI model. Package the skills and your claude code/codex/gemini agent will be an AI research agent… The licence is MIT.

When your agent uses it

  • Creating specialized models by blending domain-specific expertise (math + coding + chat)
  • Improving performance beyond single models
  • Experimenting rapidly with model variants

Example prompts

  • “/model-merging”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Merge Methods
  2. Configuration Structure
  3. Model Compatibility
  4. Weight Selection
  5. Method Selection
  6. Density Tuning (TIES/DARE)
  7. Layer-specific Merging

What it can do on your machine

Read from SKILL.md and the folder at commit 773a529. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip
    • python
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    Also links to:

    • arxiv.org
    • huggingface.co

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Model Merging loads about 3.2k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 100 tokens; SKILL.md has 491 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~100
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Orchestra-Research/AI-Research-SKILLs at commit 773a529, republished under its MIT licence (© Orchestra-Research). 491 words, ~3,192 tokens.

Download SKILL.mdSave it as .claude/skills/model-merging/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
model-merging
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.
version
1.0.0
author
Orchestra Research
license
MIT
tags
Emerging Techniques, Model Merging, Mergekit, SLERP, TIES, DARE, Task Arithmetic, Model Fusion, No Retraining, Multi-Capability, Arcee AI
dependencies
mergekit, transformers, torch

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)

SLERP (Spherical Linear Interpolation)

  • Interpolates along sphere in weight space
  • Preserves magnitude of weight vectors
  • Best for merging 2 models
  • Smoother than linear
python
# SLERP formula
merged = (sin((1-t)*θ) / sin(θ)) * model1 + (sin(t*θ) / sin(θ)) * model2
# where θ = arccos(dot(model1, model2))
# t ∈ [0, 1]

Task Arithmetic

  • Extract "task vectors" (fine-tuned - base)
  • Combine task vectors, add to base
  • Good for merging multiple specialized models (merged = base + α₁·tv₁ + α₂·tv₂)

TIES-Merging

  • Task arithmetic + sparsification
  • Resolves sign conflicts in parameters
  • Best for merging many task-specific models

DARE (Drop And REscale)

  • Randomly drops fine-tuned parameters
  • Rescales remaining parameters
  • Reduces redundancy, maintains performance
2. Configuration Structure
yaml
# Basic structure
merge_method: <method>  # linear, slerp, ties, dare_ties, task_arithmetic
base_model: <path>      # Optional: base model for task arithmetic

models:
  - model: <path/to/model1>
    parameters:
      weight: <float>   # Merge weight
      density: <float>  # For TIES/DARE

  - model: <path/to/model2>
    parameters:
      weight: <float>

parameters:
  # Method-specific parameters

dtype: <dtype>  # bfloat16, float16, float32

# Optional
slices:  # Layer-wise merging
tokenizer:  # Tokenizer configuration

Merge Methods Guide

Linear Merge

Best for: Simple model combinations, equal weighting

yaml
merge_method: linear
models:
  - model: WizardLM/WizardMath-7B-V1.1
    parameters:
      weight: 0.4
  - model: teknium/OpenHermes-2.5-Mistral-7B
    parameters:
      weight: 0.3
  - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
    parameters:
      weight: 0.3
dtype: bfloat16
SLERP Merge

Best for: Two models, smooth interpolation

yaml
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  # 0.0 = first model, 1.0 = second model
dtype: bfloat16

Layer-specific SLERP:

yaml
merge_method: slerp
slices:
  - sources:
      - model: model_a
        layer_range: [0, 32]
      - model: model_b
        layer_range: [0, 32]
parameters:
  t:
    - filter: self_attn    # Attention layers
      value: 0.3
    - filter: mlp          # MLP layers
      value: 0.7
    - value: 0.5           # Default for other layers
dtype: bfloat16
Task Arithmetic

Best for: Combining specialized skills

yaml
merge_method: task_arithmetic
base_model: mistralai/Mistral-7B-v0.1
models:
  - model: WizardLM/WizardMath-7B-V1.1  # Math
    parameters:
      weight: 0.5
  - model: teknium/OpenHermes-2.5-Mistral-7B  # Chat
    parameters:
      weight: 0.3
  - model: ajibawa-2023/Code-Mistral-7B  # Code
    parameters:
      weight: 0.2
dtype: bfloat16
TIES-Merging

Best for: Many models, resolving conflicts

yaml
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
models:
  - model: WizardLM/WizardMath-7B-V1.1
    parameters:
      density: 0.5  # Keep top 50% of parameters
      weight: 1.0
  - model: teknium/OpenHermes-2.5-Mistral-7B
    parameters:
      density: 0.5
      weight: 1.0
  - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
    parameters:
      density: 0.5
      weight: 1.0
parameters:
  normalize: true
dtype: bfloat16
DARE Merge

Best for: Reducing redundancy

yaml
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
models:
  - model: WizardLM/WizardMath-7B-V1.1
    parameters:
      density: 0.5    # Drop 50% of deltas
      weight: 0.6
  - model: teknium/OpenHermes-2.5-Mistral-7B
    parameters:
      density: 0.5
      weight: 0.4
parameters:
  int8_mask: true  # Use int8 for masks (saves memory)
dtype: bfloat16

Advanced Patterns

Layer-wise Merging
yaml
# Different models for different layers
merge_method: passthrough
slices:
  - sources:
      - model: mistralai/Mistral-7B-v0.1
        layer_range: [0, 16]   # First half
  - sources:
      - model: teknium/OpenHermes-2.5-Mistral-7B
        layer_range: [16, 32]  # Second half
dtype: bfloat16
MoE from Merged Models
yaml
# Create Mixture of Experts
merge_method: moe
base_model: mistralai/Mistral-7B-v0.1
experts:
  - source_model: WizardLM/WizardMath-7B-V1.1
    positive_prompts:
      - "math"
      - "calculate"
  - source_model: teknium/OpenHermes-2.5-Mistral-7B
    positive_prompts:
      - "chat"
      - "conversation"
  - source_model: ajibawa-2023/Code-Mistral-7B
    positive_prompts:
      - "code"
      - "python"
dtype: bfloat16
Tokenizer Merging
yaml
merge_method: linear
models:
  - model: mistralai/Mistral-7B-v0.1
  - model: custom/specialized-model

tokenizer:
  source: "union"  # Combine vocabularies from both models
  tokens:
    <|special_token|>:
      source: "custom/specialized-model"

Best Practices

1. Model Compatibility
python
# ✅ Good: Same architecture
models = [
    "mistralai/Mistral-7B-v0.1",
    "teknium/OpenHermes-2.5-Mistral-7B",  # Both Mistral 7B
]

# ❌ Bad: Different architectures
models = [
    "meta-llama/Llama-2-7b-hf",  # Llama
    "mistralai/Mistral-7B-v0.1",  # Mistral (incompatible!)
]
Show full SKILL.md (224 more words)Show less
2. Weight Selection
yaml
# ✅ Good: Weights sum to 1.0
models:
  - model: model_a
    parameters:
      weight: 0.6
  - model: model_b
    parameters:
      weight: 0.4  # 0.6 + 0.4 = 1.0

# ⚠️  Acceptable: Weights don't sum to 1 (for task arithmetic)
models:
  - model: model_a
    parameters:
      weight: 0.8
  - model: model_b
    parameters:
      weight: 0.8  # May boost performance

Unsupervised Coefficient Tuning (no labeled data needed)

Instead of manual search, use generation consistency: merge with several candidate coefficients, generate responses on a small unlabeled subset, and pick the coefficient whose outputs are most similar to those of its neighbors. Consistent outputs signal a stable, well-performing merge region (AdaMMS, arXiv:2503.23733).

python
# Pseudocode — see references/coefficient-tuning.md for full implementation
candidates = [0.3, 0.4, 0.5, 0.6, 0.7]
for alpha in candidates:
    merged_paths[alpha] = merge_with_coefficient(alpha, model_a, model_b)
    responses[alpha]    = generate_responses(merged_paths[alpha], eval_prompts)

# Score each alpha by similarity to its neighbors (alpha ± 0.1)
best_alpha = max(candidates, key=lambda a: generation_consistency(a, responses))

See references/coefficient-tuning.md for the full algorithm, similarity metrics, multi-coefficient search, and end-to-end pipeline.

3. Method Selection
python
# Choose merge method based on use case:

# 2 models, smooth blend → SLERP
merge_method = "slerp"

# 3+ models, simple average → Linear
merge_method = "linear"

# Multiple task-specific models → Task Arithmetic or TIES
merge_method = "ties"

# Want to reduce redundancy → DARE
merge_method = "dare_ties"
4. Density Tuning (TIES/DARE)
yaml
# Start conservative (keep more parameters)
parameters:
  density: 0.8  # Keep 80%

# If performance good, increase sparsity
parameters:
  density: 0.5  # Keep 50%

# If performance degrades, reduce sparsity
parameters:
  density: 0.9  # Keep 90%
5. Layer-specific Merging

Preserve the base model's first/last layers (often best left untouched) and merge only the middle via merge_method: passthrough with slices — see the Layer-wise Merging pattern above.

Evaluation & Testing

Benchmark Merged Models
python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load merged model
model = AutoModelForCausalLM.from_pretrained("./merged-model")
tokenizer = AutoTokenizer.from_pretrained("./merged-model")

# Test on various tasks
test_prompts = {
    "math": "Calculate: 25 * 17 =",
    "code": "Write a Python function to reverse a string:",
    "chat": "What is the capital of France?",
}

for task, prompt in test_prompts.items():
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=100)
    print(f"{task}: {tokenizer.decode(outputs[0])}")
Common Benchmarks
  • Open LLM Leaderboard: General capabilities
  • MT-Bench: Multi-turn conversation
  • MMLU: Multitask accuracy
  • HumanEval: Code generation
  • GSM8K: Math reasoning

Production Deployment

Save and Upload
python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load merged model
model = AutoModelForCausalLM.from_pretrained("./merged-model")
tokenizer = AutoTokenizer.from_pretrained("./merged-model")

# Upload to HuggingFace Hub
model.push_to_hub("username/my-merged-model")
tokenizer.push_to_hub("username/my-merged-model")
Quantize Merged Model
bash
# Quantize with GGUF
python convert.py ./merged-model --outtype f16 --outfile merged-model.gguf

# Quantize with GPTQ
python quantize_gptq.py ./merged-model --bits 4 --group_size 128

Common Pitfalls

  • Mismatched architectures — only merge models that share the same architecture (e.g., don't mix Llama and Mistral).
  • Over-weighting one model (e.g., 0.95 / 0.05) — keep weights balanced, typically in the 0.3–0.7 range.
  • Skipping evaluation — always benchmark a merged model before deploying (see the Evaluation & Testing section above).

Resources

See Also

  • references/methods.md - Deep dive into merge algorithms
  • references/examples.md - Real-world merge configurations
  • references/evaluation.md - Benchmarking and testing strategies
  • references/coefficient-tuning.md - Unsupervised coefficient search via generation consistency (AdaMMS, arXiv:2503.23733)

© Orchestra-Research, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (references) in 19-emerging-techniques/model-merging of Orchestra-Research/AI-Research-SKILLs.

  • SKILL.md
  • references/coefficient-tuning.md
  • references/evaluation.md
  • references/examples.md
  • references/methods.md

Open the folder on GitHubat commit 773a529

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in Orchestra-Research/AI-Research-SKILLs, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Model Merging

What does Model Merging do?

Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Model Merging is an agent skill from Orchestra-Research/AI-Research-SKILLs. Merge multiple fine-tuned models using mergekit to combine capabilities without retraining.

When should I use Model Merging?

Model Merging fits situations like: creating specialized models by blending domain-specific expertise (math + coding + chat); improving performance beyond single models; experimenting rapidly with model variants.

How do I install Model Merging in Claude Code?

Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging -a claude-code`. Or copy the skill folder (19-emerging-techniques/model-merging in Orchestra-Research/AI-Research-SKILLs) into .claude/skills/model-merging in your project. Claude Code loads it when a task matches its description.

How do I install Model Merging in Codex?

Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging -a codex`. Or copy the skill folder (19-emerging-techniques/model-merging in Orchestra-Research/AI-Research-SKILLs) into .agents/skills/model-merging in your project. Codex loads it when a task matches its description.

Can I use Model Merging in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill model-merging -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-merging, .gemini/skills/model-merging, .github/skills/model-merging and .opencode/skills/model-merging in your project.

What does Model Merging need to run?

Going by SKILL.md and its folder, Model Merging needs the command-line tools its instructions call (pip, python and git). Our summary lists: Python 3.

Does Model Merging access the network?

SKILL.md names 3 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: arxiv.org and huggingface.co. This is read from the text; nothing was executed.

Is Model Merging safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Model Merging use?

Model Merging is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Model Merging use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.

What are the alternatives to Model Merging?

Skills that share tags, products or a category with Model Merging: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), 1password (trpc-group/trpc-agent-go, 1.9k stars) and Planning With Files (jarrodwatts/claude-code-config, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Model Merging?

Orchestra-Research (a GitHub organization) maintains it in Orchestra-Research/AI-Research-SKILLs, which has 13,374 GitHub stars. The repository holds 96 skills in this directory. The repository was last updated on June 16, 2026.

Source: Orchestra-Research/AI-Research-SKILLs on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.