Agent Skills
› synthetic-sciences/openscience
› model-merging
model-merging
GitHub该技能利用mergekit等工具合并多个微调模型，无需重新训练即可融合数学、编程等多领域能力。支持SLERP、TIES-Merging等方法，旨在提升性能、降低成本并保留多技能，适用于快速实验与生产部署。
Trigger Scenarios
需要组合多个模型的特定能力
希望在不重新训练的情况下创建专用模型
试图通过模型融合超越单一模型的性能瓶颈
寻求降低GPU训练成本或加速模型变体实验
Install
npx skills add synthetic-sciences/openscience --skill model-merging -g -y
SKILL.md
Frontmatter
{
"name": "model-merging",
"tags": [
"Emerging Techniques",
"Model Merging",
"Mergekit",
"SLERP",
"TIES",
"DARE",
"Task Arithmetic",
"Model Fusion",
"No Retraining",
"Multi-Capability",
"Arcee AI"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "ml-training",
"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.",
"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
# 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(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
# Task vector
task_vector = finetuned_model - base_model
# Merge multiple task vectors
merged = base_model + α₁*task_vector₁ + α₂*task_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 algorithmsreferences/examples.md- Real-world merge configurationsreferences/evaluation.md- Benchmarking and testing strategies
Version History
- e9844a4 Current 2026-07-11 17:29
Dependencies
-
required
mergekit - required transformers
-
required
torch
