Improve model card and add metadata

README.md

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 ---
+license: apache-2.0
 library_name: transformers
-tags: []
+base_model: meta-llama/Llama-3.2-3B
+pipeline_tag: text-generation
+datasets:
+- locuslab/TOFU
+tags:
+- machine-unlearning
+- lora
+- peft
+- unlearning
 ---
 
-# Model Card for Model ID
+# MAAT: Multi-phase Adapter-Aware Targeted Unlearning
 
-<!-- Provide a quick summary of what the model is/does. -->
+This repository contains a LoRA adapter for `meta-llama/Llama-3.2-3B` that has been unlearned using the **MAAT** framework.
 
+## Model Description
 
+MAAT (Multi-phase Adapter-Aware Targeted Unlearning) is a three-phase framework designed to address the challenges of "Why-type" questions—probing causal and relational knowledge—in machine unlearning. Standard unlearning evaluation is often skewed toward simple factual lookups, whereas MAAT focuses on complex reasoning chains and gradient dilution over long answer spans.
 
-## Model Details
+The framework operates exclusively on LoRA adapter weights through three distinct phases:
+1. **Phase 1 — Gradient Policy Ascent:** Uses orthogonal projection to remove components of the retain gradient from the forget gradient.
+2. **Phase 2 — Structural Compression and Task Negation:** Employs SVD rank-dimension pruning to mask forget-scored dimensions and task vector negation.
+3. **Phase 3 — Multi-Objective Utility Repair Engine:** Restores model utility on the retain set using a hybrid KL-hidden-state repair mechanism.
 
-### Model Description
+This specific checkpoint is targeted at unlearning components of the **TOFU** dataset while maintaining high retention on general knowledge.
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+- **Developed by:** Suryash Yagnik, Shubham Gaur, Saksham Thakur, Vinija Jain, Aman Chadha, Amitava Das
+- **Paper:** [MAAT: Multi-phase Adapter-Aware Targeted Unlearning](https://huggingface.co/papers/2605.30514)
+- **Repository:** [GitHub - SuryanshYagnik/Machine-Unlearning](https://github.com/SuryanshYagnik/Machine-Unlearning)
+- **Base Model:** [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B)
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
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-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
-
-[More Information Needed]
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
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-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
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-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+The framework was evaluated using **5WBENCH**, a balanced 5,000-sample benchmark with 1,000 examples per 5W category (Who, What, When, Where, Why), allowing for the quantification of causal unlearning failures.
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
+```bibtex
+@article{yagnik2024maat,
+  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={2024}
+}
+```