model-merging

# 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 - Sph...