Novaspree/factify-Gemma3-adapter-1 API & Inference Endpoint

Model Description

MAAT is designed to address the challenges of causal unlearning (answering "Why"-type questions) while maintaining high retention of other knowledge. The framework operates exclusively on LoRA adapter weights and achieves a new operating point on the forget-retain Pareto frontier by combining:

Gradient-Projected Ascent: Orthogonally projects forget gradients to remove components that conflict with the retain set.
Structural Compression and Task Negation: Uses SVD rank-dimension pruning and task vector negation to selectively erase knowledge.
Multi-Objective Utility Repair Engine: A joint parameter alignment loop over the retain set to recover utility using KL divergence and hidden-state alignment.

Developed by: Suryash Yagnik, Shubham Gaur, Saksham Thakur, Vinija Jain, Aman Chadha, Amitava Das
Base Model: google/gemma-3-4b-it
Paper: MAAT: Multi-phase Adapter-Aware Targeted Unlearning
Repository: GitHub: Machine-Unlearning

Evaluation

The model was evaluated using 5WBENCH, a balanced 5,000-sample benchmark covering Who, What, When, Where, and Why categories, specifically designed to quantify causal unlearning failures.

Citation

bibtex
@article{yagnik2025maat,
  title={MAAT: Multi-phase Adapter-Aware Targeted Unlearning},
  author={Yagnik, Suryash and Gaur, Shubham and Thakur, Saksham and Jain, Vinija and Chadha, Aman and Das, Amitava},
  journal={arXiv preprint arXiv:2605.30514},
  year={2025}
}

Model Description

Gradient-Projected Ascent: Orthogonally projects forget gradients to remove components that conflict with the retain set.
Structural Compression and Task Negation: Uses SVD rank-dimension pruning and task vector negation to selectively erase knowledge.
Multi-Objective Utility Repair Engine: A joint parameter alignment loop over the retain set to recover utility using KL divergence and hidden-state alignment.

Developed by: Suryash Yagnik, Shubham Gaur, Saksham Thakur, Vinija Jain, Aman Chadha, Amitava Das
Base Model: google/gemma-3-4b-it
Paper: MAAT: Multi-phase Adapter-Aware Targeted Unlearning
Repository: GitHub: Machine-Unlearning

Evaluation

The model was evaluated using 5WBENCH, a balanced 5,000-sample benchmark covering Who, What, When, Where, and Why categories, specifically designed to quantify causal unlearning failures.

Citation

bibtex
@article{yagnik2025maat,
  title={MAAT: Multi-phase Adapter-Aware Targeted Unlearning},
  author={Yagnik, Suryash and Gaur, Shubham and Thakur, Saksham and Jain, Vinija and Chadha, Aman and Das, Amitava},
  journal={arXiv preprint arXiv:2605.30514},
  year={2025}
}

factify-Gemma3-adapter-1

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