Model Merging

Merge models with 16 strategies — SLERP, TIES, DARE, Task Arithmetic, and more.

PMetal supports 16 model merging strategies (12 via CLI, 4 library-only). Features GPU-accelerated merging, FP8-aware operations, and async double-buffered streaming for large models.

CLI 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

Library-Only Strategies

Strategy	Description
RamMerge	RAM merge strategy
SouperMerge	Souper merge strategy
MultiSlerpMerge	Multi-model SLERP

Examples

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

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

# DARE-TIES
pmetal merge --models model-a model-b --method dare_ties --density 0.7

Advanced Features

• GPU-Accelerated Merging: Metal-based merge operations for large models
• FP8-Aware Merging: Merge with FP8 quantization for memory efficiency
• Async Merge Pipeline: Double-buffered streaming for models that exceed memory
• LoRA Fusing: Merge LoRA adapters into base weights (standard and accurate modes)

See Also

• pmetal merge — CLI reference
• pmetal fuse — LoRA fusing