# Mergekit > --- - Skill: `qcmuu/mergekit` (Agent Skill) - Install (CLI): `npx skillmds@latest add qcmuu/mergekit` - Raw SKILL.md: https://api.skillmd.com/api/skills/qcmuu/mergekit/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: qcmuu (https://skillmd.com/u/qcmuu) - Updated: 2026-08-19 - Page: https://skillmd.com/skills/qcmuu/mergekit --- --- title: "Mergekit" description: "Toolkit for merging multiple fine-tuned language models without requiring GPU compute. Supports SLERP, TIES, DARE, Task Arithmetic, Frankenmerging (layer stacking), and Evolutionary merge methods. Used to combine specialized models, reduce catastrophic forgetting, and create generalist models from specialists." skillName: "mergekit" skillVersion: "1.0.0" skillAuthor: "Orchestra Research" skillLicense: "MIT" skillTags: ["Model Merging", "Mergekit", "SLERP", "TIES", "DARE", "Task Arithmetic", "Frankenmerge", "Model Fusion", "No GPU Required"] skillDeps: ["mergekit", "transformers", "torch", "pyyaml"] --- | | | |---|---| | **Version** | 1.0.0 | | **Author** | Orchestra Research | | **License** | MIT | | **Tags** | `Model Merging` `Mergekit` `SLERP` `TIES` `DARE` `Task Arithmetic` | | **Dependencies** | `mergekit` `transformers` `torch` | # Mergekit — Merge LLMs Without Training Combine specialized fine-tuned models into a single capable model. No gradient computation required. ## When to use Mergekit **Use Mergekit when:** - Combining domain-specific fine-tunes (coding + math + reasoning) - Avoiding catastrophic forgetting from sequential fine-tuning - Creating generalist models from specialist checkpoints - No GPU budget for continued training - Experimenting with model fusion research **Metrics**: - **5,000+ GitHub stars** - No GPU required for most merge methods (CPU only) - Supports all popular architectures (Llama, Mistral, Qwen, Falcon, BLOOM) - Used to create top-ranked Open LLM Leaderboard models ## Quick start ```bash pip install mergekit # Or from source (for latest methods) git clone https://github.com/arcee-ai/mergekit cd mergekit && pip install -e . ``` ## Merge methods ### SLERP — smooth interpolation between two models ```yaml # slerp_merge.yaml models: - model: mistralai/Mistral-7B-Instruct-v0.2 parameters: weight: 0.6 - model: HuggingFaceH4/zephyr-7b-beta parameters: weight: 0.4 merge_method: slerp base_model: mistralai/Mistral-7B-v0.1 dtype: bfloat16 ``` ```bash mergekit-yaml slerp_merge.yaml ./merged-model --copy-tokenizer ``` ### TIES — handle parameter conflicts from multiple models ```yaml # ties_merge.yaml models: - model: meta-llama/Llama-3.1-8B-Instruct parameters: density: 0.7 # trim 30% of delta weights weight: 1.0 - model: my-org/llama-3.1-8b-math-finetuned parameters: density: 0.7 weight: 1.0 - model: my-org/llama-3.1-8b-code-finetuned parameters: density: 0.7 weight: 1.0 merge_method: ties base_model: meta-llama/Llama-3.1-8B dtype: bfloat16 parameters: normalize: true ``` ### DARE — sparsify before merging (reduces interference) ```yaml merge_method: dare_ties base_model: meta-llama/Llama-3.1-8B models: - model: my-org/llama-math parameters: density: 0.5 weight: 1.0 - model: my-org/llama-code parameters: density: 0.5 weight: 1.0 dtype: bfloat16 ``` ### Task Arithmetic — add/subtract capabilities ```yaml # Boost math, reduce toxicity merge_method: task_arithmetic base_model: meta-llama/Llama-3.1-8B models: - model: my-org/llama-math parameters: weight: 1.5 # amplify math - model: my-org/llama-toxic parameters: weight: -1.0 # subtract toxicity dtype: bfloat16 ``` ### Frankenmerge — layer stacking (creates larger models) ```yaml # Stack layers from two 7B models → 14B "Frankenstein" model merge_method: passthrough slices: - sources: - model: mistralai/Mistral-7B-v0.1 layer_range: [0, 16] # first 16 layers - sources: - model: HuggingFaceH4/zephyr-7b-beta layer_range: [16, 32] # last 16 layers dtype: bfloat16 ``` ## Python API ```python import yaml from mergekit.config.models import MergeConfiguration from mergekit.merge import MergeOptions, run_merge # Define merge config config_dict = { "merge_method": "slerp", "models": [ {"model": "mistralai/Mistral-7B-Instruct-v0.2", "parameters": {"weight": 0.6}}, {"model": "HuggingFaceH4/zephyr-7b-beta", "parameters": {"weight": 0.4}}, ], "base_model": "mistralai/Mistral-7B-v0.1", "dtype": "bfloat16", } config = MergeConfiguration.model_validate(config_dict) run_merge( merge_config=config, out_path="./merged-model", options=MergeOptions( copy_tokenizer=True, lazy_unpickle=True, low_cpu_memory=True, # stream weights to reduce RAM usage ) ) ``` ## Evolutionary Merge (EvoMerge) ```python from mergekit.evo.config import EvolMergeConfiguration from mergekit.evo.runner import EvolMergeRunner # Auto-optimize merge weights using CMA-ES config = EvolMergeConfiguration( models=["my-org/model-a", "my-org/model-b", "my-org/model-c"], base_model="meta-llama/Llama-3.1-8B", merge_method="dare_ties", evaluation_task="arc_challenge", num_generations=50, population_size=10, ) runner = EvolMergeRunner(config, device="cuda") best_config, best_score = runner.run() print(f"Best merge score: {best_score:.4f}") print(best_config) ``` ## Common pitfalls - **Architecture mismatch**: All merged models must share the same base architecture - **OOM on merge**: Use `--low-cpu-memory` flag for large models - **Tokenizer conflicts**: Use `--copy-tokenizer` from the best tokenizer model - **SLERP with 3+ models**: Use TIES or DARE instead; SLERP is binary only ## References - [Mergekit GitHub](https://github.com/arcee-ai/mergekit) - [TIES paper](https://arxiv.org/abs/2306.01708) - [DARE paper](https://arxiv.org/abs/2311.03099) - [EvoMerge paper](https://arxiv.org/abs/2403.13187)