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Model-Merging-Interference-Risk-Dataset

Simulated retained-capability spot-checks for 864 model-merging configurations across 6 methods (linear averaging, Task Arithmetic, TIES, DARE-TIES, DELLA, Model Stock), 3 expert-heterogeneity levels, expert count, and model scale, grounded in the foundational 2022-2023 model-merging literature through 2025-2026 large-scale empirical studies. Forward-looking task predicts full-suite interference from a quick spot-check on a handful of tasks.

4.50

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updated 12 Aug 2026

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COVERAGE India

LICENSE Attribution 4.0 International (CC BY- 4.0)

FORMAT Synthetic

TYPE Structured

SECTOR Science, Technology and Research

UPDATES NA

About Dataset

Simulates whether merging fine-tuned experts preserves or destroys their individual capabilities. 864 configs across 6 methods including a documented non-monotonic failure mode (DARE-TIES is too aggressive on well-aligned models specifically). Ten sources (2022-2026) in merging_theory.py, including Model Soups, Task Arithmetic, TIES-Merging, DARE, DELLA, and a large "in-the-wild" study (4 base LLMs × 12 fine-tunes × 16 tasks).

Dataset Metadata

License

Attribution 4.0 International (CC BY- 4.0)

Sector

Science, Technology and Research

Author

Jai Mali

Source Organisation

Symbiosis Skills and Professional University

Uploaded By

Data Quality Score (Beta)

4.5

Dataset Type

Structured

Data Type

Synthetic

Task Category

N.A.

Time Granularity

NA

Frequency

NA

Geographic Granularity

N.A.

Primary Key / Indicator

N.A.

Data Collection Method

N.A.

Language

N.A.

Year Range

N.A.

Date & Time

09/08/26 17:21:09

Visibility

Open

Hosted / Redirected

Hosted

Tags

Large Language Model

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