mergelens 0.1.3

Pre-merge diagnostic framework for LLM model merging — analyze, diagnose, and optimize before you merge.

MergeLens

Pre-merge diagnostics for LLM model merging

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34% of top Open LLM Leaderboard models are merges, yet merging is blind trial-and-error. MergeLens tells you before you merge whether it will work — and which method to use.

Features

• Single compatibility score — Merge Compatibility Index (MCI): 0-100, go/no-go verdict
• 10 diagnostic metrics — cosine similarity, spectral overlap, sign disagreement, TSV interference, CKA, and more
• Strategy recommender — optimal merge method + ready-to-paste MergeKit YAML
• Conflict zone detection — pinpoints problematic layers
• Interactive HTML reports — self-contained Plotly dashboards
• MCP server — AI assistants can diagnose merges natively
• Memory efficient — lazy safetensors loading, peak memory = 2× largest layer

Install

pip install mergelens

Optional extras:

pip install mergelens[mcp]    # MCP server for AI assistants
pip install mergelens[audit]  # Capability probing (requires transformers)
pip install mergelens[all]    # Everything

Quick Start

CLI

Compare two models (local paths or HuggingFace Hub IDs):

mergelens compare model_a/ model_b/
mergelens compare meta-llama/Llama-3-8B mistralai/Mistral-7B-v0.1

Add a base model for task vector metrics:

mergelens compare model_a/ model_b/ --base base_model/

Generate an HTML report:

mergelens compare model_a/ model_b/ --report report.html

Diagnose a MergeKit config before running it:

mergelens diagnose --config merge.yaml

Python API

from mergelens import compare_models

result = compare_models(["model_a/", "model_b/"])

print(f"MCI: {result.mci.score} — {result.mci.verdict}")
# MCI: 72.3 — compatible

Inspect conflicts and get a strategy recommendation:

for zone in result.conflict_zones:
    print(f"Layers {zone.start_layer}-{zone.end_layer}: {zone.severity.value}")

if result.strategy:
    print(f"Recommended: {result.strategy.method.value}")
    print(result.strategy.mergekit_yaml)  # copy-paste into MergeKit

Diagnose a MergeKit config:

from mergelens import diagnose_config

result = diagnose_config("merge.yaml")
print(f"Overall interference: {result.overall_interference:.4f}")

Metrics

Metric	What It Measures	Range	Source
Cosine Similarity	Weight vector alignment	[-1, 1]	Standard
L2 Distance	Normalized weight divergence	[0, +inf)	Standard
KL Divergence	Weight distribution difference	[0, +inf)	Standard
Spectral Subspace Overlap	Top-k SVD direction alignment	[0, 1]	Zhou et al. 2026
Effective Rank Ratio	Dimensionality compatibility	[0, 1]	Shannon entropy
Sign Disagreement Rate	Parameter sign conflicts	[0, 1]	TIES-Merging (Yadav et al. 2023)
TSV Interference	Cross-task singular vector conflict	[0, +inf)	Gargiulo et al. 2025
Task Vector Energy	Knowledge concentration in top SVs	[0, 1]	Choi et al. 2024
CKA Similarity	Activation representation similarity	[0, 1]	Kornblith et al. 2019
Merge Compatibility Index	Composite go/no-go score	[0, 100]	Ours

MCI Verdicts

Score	Verdict	Meaning
75-100	Highly Compatible	Merge with confidence
55-74	Compatible	Should work, monitor quality
35-54	Risky	Expect degradation, use targeted methods
0-34	Incompatible	These models likely shouldn't be merged

Strategy Recommendations

MergeLens maps diagnostic profiles to merge methods. Different metrics predict success for different methods (Zhou et al. 2026 found only 46.7% metric overlap between methods):

Diagnostic Profile	Recommended Method
High cosine similarity everywhere	SLERP
High sign disagreement (>30%)	TIES
Concentrated task vector energy	DARE
Low spectral overlap	Linear (small alpha)

Each recommendation includes a ready-to-paste MergeKit YAML config.

MCP Integration

{
  "mcpServers": {
    "mergelens": {
      "command": "mergelens",
      "args": ["serve"]
    }
  }
}

Tools: compare_models, diagnose_merge, get_conflict_zones, suggest_strategy, generate_report, explain_layer, get_compatibility_score

How It Works

MergeLens loads model weights lazily via memory-mapped safetensors (peak memory: 2× largest layer, not 2× full model). It computes metrics layer-by-layer, detects conflict zones, and aggregates everything into the MCI score.

Security: No pickle/torch.load (safetensors only), yaml.safe_load(), tensor size limits, no credential leakage.

References

• Zhou et al. 2026, "Demystifying Mergeability of Homologous LLMs" (arXiv:2601.22285)
• Gargiulo et al. CVPR 2025, "Task Singular Vectors" (arXiv:2412.00081)
• Yadav et al. NeurIPS 2023, "TIES-Merging" (arXiv:2306.01708)
• Choi et al. 2024, "Revisiting Weight Averaging for Model Merging" (arXiv:2412.12153)
• Kornblith et al. 2019, "Similarity of Neural Network Representations Revisited"
• Rahamim et al. 2026, "Will it Merge?" (arXiv:2601.06672)

License

Apache 2.0