Training
Model merging (mergekit)
Edit or adapt a modelMergingReferenceFuse multiple finetunes of the same base into one checkpoint by interpolating or sparsely merging their weights — no training, no extra params.
On the Spark
Merges are just tensor math on the 128 GB pool — the whole community's favorite way to make a new model without a GPU cluster.
- Objective
- Edit or adapt a model
- Targets
- Weights
- Format
- SLERP / TIES / DARE weight interpolation
- Granularity
- Whole-model, per-tensor weighting
- Lifecycle
- PTQ · no gradients
- Calibration
- No calibration
- Compression
- None — edits behavior, not size
- Quality
- Blends the parents' capabilities; quality depends on recipe
- Hardware
- None — offline weight edit, runs on any runtime
- Runtimes
A worked Spark recipe for this method hasn't been written yet — it lives here as a reference point in the ontology.