name: SuperMerge-LayeredTIES-v1
merge_method: della_linear
base_model: CultriX/Enhanced-TIES-Base-v1 # Referencing the TIES base model defined below (now inlined)
tokenizer_source: base
dtype: float32
out_dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
  rescale: false
  t: [0.1, 0.3, 0.7, 0.7, 0.4, 0.2]

slices:
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [0, 8]
        parameters:
          weight: 0.7
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [0, 8]
        parameters:
          weight: 0.3
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [0, 8]
        parameters:
          weight: 0.0
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [0, 8]
        parameters:
          weight: 0.0
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [8, 16]
        parameters:
          weight: 0.4
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [8, 16]
        parameters:
          weight: 0.3
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [8, 16]
        parameters:
          weight: 0.3
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [8, 16]
        parameters:
          weight: 0.0
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [16, 24]
        parameters:
          weight: 0.2
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [16, 24]
        parameters:
          weight: 0.2
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [16, 24]
        parameters:
          weight: 0.5
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [16, 24]
        parameters:
          weight: 0.1
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [24, 32]
        parameters:
          weight: 0.25
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [24, 32]
        parameters:
          weight: 0.1
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [24, 32]
        parameters:
          weight: 0.4
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [24, 32]
        parameters:
          weight: 0.25
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [32, 40]
        parameters:
          weight: 0.4
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [32, 40]
        parameters:
          weight: 0.0
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [32, 40]
        parameters:
          weight: 0.2
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [32, 40]
        parameters:
          weight: 0.4
  - sources:
      - model: CultriX/Enhanced-TIES-Base-v1 # Referencing inlined TIES base
        layer_range: [40, 48]
        parameters:
          weight: 0.6
      - model: arcee-ai/Virtuoso-Small-v2
        layer_range: [40, 48]
        parameters:
          weight: 0.0
      - model: sthenno/tempesthenno-ppo-ckpt40
        layer_range: [40, 48]
        parameters:
          weight: 0.1
      - model: sometimesanotion/Qwenvergence-14B-v3-Prose
        layer_range: [40, 48]
        parameters:
          weight: 0.3



# Commentary:
# =============================================================================
# SuperMerge-LayeredTIES-v1 Commentary:
#
# This configuration combines the strengths of both Enhanced-LayeredSlerp-v1 and SuperMerge-Enhanced-v1.
# It leverages the robust foundation of a TIES-merged base model (Enhanced-TIES-Base-v1) and applies
# the layer-wise module approach and fine-grained weight control from SuperMerge-Enhanced-v1 in a SLERP merge.
#
# Key Features:
#   - TIES-Merged Base Foundation:  Uses 'Enhanced-TIES-Base-v1' as the base model for the SLERP merge.
#     This TIES base provides a selectively merged and potentially more efficient starting point, incorporating
#     strengths from multiple models (Virtuoso, Phi-4, Qwenvergence, DeepSeek) with density control.
#
#   - Layer-wise Module Integration in SLERP:  Maintains the module-based slice structure from SuperMerge-Enhanced-v1.
#     The SLERP merge now combines the TIES-merged base with specialized modules for Reasoning, IFEval, and MATH/Knowledge
#     at different layer ranges, using explicit weights for fine-grained control.
#
#   - Benchmark-Driven Iterative Weight Tuning:  The configuration is designed to be optimized through a
#     benchmark-driven iterative weight tuning process (as described in the refined SuperMerge-Enhanced-v1 approach).
#     The initial weights provided are starting points and need to be systematically tuned based on benchmark results.
#
# Tuning Process (Same as Refined SuperMerge-Enhanced-v1):
#   1. Initial Benchmarking: Run a full benchmark suite.
#   2. Performance Analysis: Examine per-benchmark scores and compare to source models.
#   3. Targeted Weight Adjustments: Adjust layer weights based on performance analysis (e.g., increase IFEval module weight
#      in early layers if IFEval is weak).
#   4. Iterate: Repeat steps 1-3. Make small, incremental adjustments in each iteration.
#
# Rationale:
#   - By using a TIES-merged base, we aim to create a more robust and potentially efficient foundation for the SLERP merge.
#   - The layer-wise module approach and fine-grained weights in SLERP still allow for precise control over the blending
#     of specialized capabilities at different network depths, building upon the solid TIES base.
#   - The emphasis on a benchmark-driven iterative weight tuning process remains crucial for achieving optimal performance.
#
# Next Steps:
#   - Implement this configuration using MergeKit.
#   - Run initial benchmarks to establish a baseline.
#   - Begin the iterative benchmark-driven weight tuning process to optimize performance.
# =============================================================================