Merge multiple LLM checkpoints and LoRA deltas into a single .safetensors artifact.
npx skills add https://github.com/thistleknot/skills --skill mergekit-skill
Please help me install this Agent Skill. Skill: mergekit — Skill Source: https://github.com/thistleknot/skills/tree/main/mergekit Command: npx skills add https://github.com/thistleknot/skills --skill mergekit-skill
It solves the problem of combining multiple pretrained LLM checkpoints, fine-tunes, or LoRA adapters into a single merged .safetensors artifact without doing full retraining.
Use mergekit to merge two LoRA- or fine-tuned checkpoints into a single .safetensors model by telling the AI to run the mergekit-yaml CLI on a provided YAML config that specifies the merge method, base model (when required), input model paths, and output directory.
High-intent search queries and answers about installing and using this skill.
You can merge fine-tuned LLM checkpoints into a single model by using a YAML configuration to specify merge methods like linear blending or task arithmetic, outputting a combined safetensors file without full retraining.
Yes, out-of-core tensor streaming allows you to merge large models with limited RAM by processing and writing tensors layer-by-layer instead of loading entire checkpoints into memory simultaneously.
TIES and DARE reduce parameter interference during delta merging, while frankenmerging assembles a new model by selecting and blending contiguous layer ranges from different pretrained source checkpoints.
Checkpoint blending supports LoRA extraction by approximating finetune deltas, allowing you to derive and merge LoRA adapters alongside other pretrained models using a mergekit YAML configuration.
Yes, task arithmetic and similar interference-reducing merge methods require a base model in the configuration to calculate parameter deltas correctly and ensure valid composition.
Slerp and nuslerp interpolation methods for checkpoint blending require merging exactly two models at a time and need correct parameter and weight semantics to function properly.
qcmuuMerge fine-tuned language model checkpoints into a single fused model using YAML configurations.
amdCreate export and evaluation plans for Quark quantized models.
VKirillLoad and run HuggingFace Transformers models for inference and fine-tuning.
granietFine-tune 7B-70B LLMs with LoRA and QLoRA on consumer GPUs.
