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

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

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

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)

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

merged_weights = w1 * model1_weights + w2 * model2_weights + w3 * model3_weights

where w1 + w2 + w3 = 1

SLERP (Spherical Linear Interpolation)

• Interpolates along sphere in weight space

• Preserves magnitude of weight vectors

• Best for merging 2 models

• Smoother than linear

SLERP formula

merged = (sin((1-t)θ) / sin(θ)) * model1 + (sin(tθ) / 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

Task vector

task_vector = finetuned_model - base_model

Merge multiple task vectors

merged = base_model + α₁task_vector₁ + α₂task_vector₂

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

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

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

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:

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

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

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

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

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

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

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

✅ 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!) ]

2. Weight Selection

✅ 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

3. Method Selection

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)

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 base model's beginning and end

merge_method: passthrough slices:

• sources:
  • model: base_model layer_range: [0, 2] # Keep first layers
• sources:
  • model: merged_middle # Merge middle layers layer_range: [2, 30]
• sources:
  • model: base_model layer_range: [30, 32] # Keep last layers

Evaluation & Testing

Benchmark Merged Models

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

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

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

❌ Pitfall 1: Merging Incompatible Models

Wrong: Different architectures

models:

• model: meta-llama/Llama-2-7b # Llama architecture
• model: mistralai/Mistral-7B # Mistral architecture

Fix: Only merge models with same architecture

❌ Pitfall 2: Over-weighting One Model

Suboptimal: One model dominates

models:

• model: model_a parameters: weight: 0.95 # Too high
• model: model_b parameters: weight: 0.05 # Too low

Fix: Use more balanced weights (0.3-0.7 range)

❌ Pitfall 3: Not Evaluating

Wrong: Merge and deploy without testing

mergekit-yaml config.yml ./merged-model

Deploy immediately (risky!)

Fix: Always benchmark before deploying

Resources

• mergekit GitHub: https://github.com/arcee-ai/mergekit

• HuggingFace Tutorial: https://huggingface.co/blog/mlabonne/merge-models

• LazyMergekit: Automated merging notebook

• TIES Paper: https://arxiv.org/abs/2306.01708

• DARE Paper: https://arxiv.org/abs/2311.03099

See Also

• references/methods.md

• Deep dive into merge algorithms

• references/examples.md

• Real-world merge configurations

• references/evaluation.md

• Benchmarking and testing strategies