pmetal merge

Merge multiple models into one using various merge strategies. Supports GPU-accelerated merging, FP8-aware merging, and async double-buffered streaming for large models.

Usage

pmetal merge \
  --models <MODEL_A> <MODEL_B> [<MODEL_C>...] \
  --method <METHOD> \
  [OPTIONS]

Examples

# SLERP merge
pmetal merge \
  --models model-a model-b \
  --method slerp --t 0.5

# TIES merge with sparsification
pmetal merge \
  --models base-model ft-model-1 ft-model-2 \
  --method ties --density 0.5

# DARE-TIES with random pruning
pmetal merge \
  --models model-a model-b \
  --method dare_ties --density 0.7

Strategies

Method	Description
linear	Simple weighted averaging
slerp	Spherical linear interpolation
ties	Task arithmetic with sparsification and sign consensus
dare_ties	Random pruning with rescaling (TIES variant)
dare_linear	Random pruning with rescaling (linear variant)
task_arithmetic	Task vector arithmetic
della	Adaptive magnitude-based pruning
della_linear	Adaptive magnitude pruning (linear variant)
breadcrumbs	Breadcrumbs merge strategy
model_stock	Geometric interpolation based on task vector similarity
nearswap	Near-swap merge strategy
passthrough	Layer passthrough composition

Additional library-only strategies: RamMerge, SouperMerge, KarcherMerge, MultiSlerpMerge.

See Also

• Model Merging — Detailed merge documentation