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Model merging with mergekit v1.0.0
Use this resource to merge several fine-tuned models without retraining them
New
- 406 downloads
- 200 likes
- 145 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install llm-model-merging-mergekitWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent writes the mergekit configuration that combines your models (linear, SLERP, TIES, DARE or task arithmetic), runs the merge on CPU and helps you evaluate the result. You get a configuration file and testing steps.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: installation, linear merge and SLERP merge for two models
- references/methods.md: merge methods and their settings
- references/examples.md and references/evaluation.md: configuration examples and evaluation
Good fit for
- You want to combine math and coding skills in one model
- You test model variants quickly without a GPU
Not for
Tags
- model merging
- mergekit
- slerp
- ties
About
You say
“Merge these two fine-tuned models with SLERP and prepare the mergekit configuration.”
Skill published by davila7 under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to merge several fine-tuned models without retraining them
- Review and adjust the result
Content preview
Files
- LICENSE1.1 KB
- NOTICE-PALATEKIT.md0.5 KB
- references/evaluation.md11.9 KB
- references/examples.md9 KB
- references/methods.md9.7 KB
- SKILL.md12.7 KB
Origin and licence
Listed unmodified
- Author
- davila7
- Source
- github.com/davila7/claude-code-templates/tree/f57964ebda91cdfb31755661ecea9395f30020f1/cli-tool/components/skills/ai-research/emerging-techniques-model-merging
- Licence
- MIT
Notice
« emerging-techniques-model-merging » (davila7, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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