Metadata-Version: 2.4
Name: forgetium
Version: 0.2.0
Summary: A professional Python library for machine unlearning of Large Language Models.
Project-URL: Homepage, https://github.com/ziadsalama95/forgetium
Project-URL: Documentation, https://ziadsalama95.github.io/forgetium
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Author-email: Ziad Salama <ziadsalama95@gmail.com>
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License-File: LICENSE
Keywords: ai-safety,language-models,llm,machine-unlearning,muse,privacy,pytorch,right-to-be-forgotten,tofu,transformers,wmdp
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Description-Content-Type: text/markdown

# Forgetium

> **Machine unlearning for Large Language Models — without retraining.**

[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](LICENSE)
[![Python](https://img.shields.io/badge/python-3.11%2B-blue.svg)](https://www.python.org)
[![PyPI](https://img.shields.io/pypi/v/forgetium.svg)](https://pypi.org/project/forgetium/)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.1%2B-ee4c2c.svg)](https://pytorch.org)
[![Transformers](https://img.shields.io/badge/transformers-4.40%2B-yellow.svg)](https://huggingface.co/docs/transformers)

Forgetium is a professional, research-grade Python library for **post-hoc machine unlearning** of HuggingFace causal language models. When your model memorized something it shouldn't — a fact, a copyrighted passage, a piece of PII, a hazardous procedure — Forgetium gives you **principled, peer-reviewed algorithms** to make it forget, without retraining from scratch.

---

## Install

```bash
pip install forgetium
```

## The 3-line example

```python
from forgetium import unlearn

result = unlearn(
    model="Qwen/Qwen2.5-0.5B-Instruct",
    forget="What is the capital of France?",
)

print("Before:", result.before["What is the capital of France?"])
print("After: ", result.after["What is the capital of France?"])
```

Output:

```text
Before: The capital of France is Paris.
After:  I'm not sure about that.
```

That's it. **No dataset preparation, no hyperparameter tuning, no method picking.** Forgetium loads the model, builds a retain set automatically by self-prompting the model with diverse questions, picks an appropriate method and hyperparameters based on the model size, runs unlearning, and hands you back the modified model.

The model in `result.model` is a regular `transformers.PreTrainedModel` you can save with `result.save("./out")`, push with `result.push_to_hub("you/repo")`, or use directly.

---

## What it actually does in the background

When you call `unlearn(model=..., forget=...)`:

1. **Loads** the model and tokenizer from HuggingFace (or accepts a pre-loaded model + your `hf_token` for gated models).
2. **Normalizes the forget input**:
    - questions → asks the model and uses its own answer as the response to forget;
    - facts → uses the model to invent a question that the fact would answer;
    - explicit `(prompt, response)` pairs → uses them as-is.
3. **Builds the retain set automatically** by self-prompting the model on a curated bank of ~180 diverse questions, with a topic-overlap filter so retain anchors are guaranteed orthogonal to the forget topic.
4. **Auto-picks hyperparameters**: learning rate, epochs, batch size, mixed precision (bf16 / fp16 / fp32), method (`preference_optimization` for clean refusals, `npo` for genuine knowledge removal), `retain_weight`, and whether to use **LoRA** (default for ≥1B-param models) or full fine-tuning.
5. **Captures `before` answers** for every forget prompt.
6. **Runs unlearning** with a progress log.
7. **Captures `after` answers** so you can sanity-check immediately.
8. Returns an `UnlearnResult` with `.model`, `.tokenizer`, `.history`, `.metrics`, `.retain_records`, `.before`, `.after`, `.test()`, `.save()`, `.push_to_hub()`, and `.summary()`.

---

## More common patterns

### Forget multiple facts and push to the Hub

```python
result = unlearn(
    model="Qwen/Qwen2.5-0.5B-Instruct",
    forget=[
        "What is the capital of France?",
        "Who painted the Mona Lisa?",
    ],
    push_to_hub="ziadsalama95/qwen-no-paris-monalisa",
)
```

### Genuine knowledge removal (NPO) instead of surface refusal

```python
result = unlearn(
    model="Qwen/Qwen2.5-0.5B-Instruct",
    forget="What is the capital of France?",
    mode="knowledge",   # → uses NPO under the hood
)
```

### Test arbitrary prompts after unlearning

```python
print(result.test("Tell me about Paris"))            # forgotten
print(result.test("Who wrote Hamlet?"))              # still works
```

### Built-in evaluation metrics

```python
result = unlearn(..., evaluate=True)
print(result.metrics)
# {'forget_perplexity': 15.7, 'retain_perplexity': 5.4,
#  'forget_truth_ratio': 0.09, 'mia_loss_auroc_distinguishability': 1.0, ...}
```

### Override anything

```python
result = unlearn(
    model="Qwen/Qwen2.5-0.5B-Instruct",
    forget="...",
    method="kl_minimization",                   # any of the 5 built-in methods
    use_lora=False,                              # force full fine-tune
    n_retain=100,                                # bigger anchor
    config_overrides={"learning_rate": 5e-5},
    method_kwargs={"kl_weight": 0.5},
)
```

---

## Advanced usage (research-grade, low-level API)

If you're writing a thesis chapter or running ablations, the original low-level API is still there. You construct the dataset, pick the method explicitly, and have full control over every hyperparameter.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from forgetium import (
    NPOUnlearner, UnlearningConfig,
    QADataset, ForgetRetainDataset, Evaluator, compare_methods,
)

tok   = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")

forget = QADataset.from_pairs([("What is the capital of France?", "Paris.")])
retain = QADataset.from_pairs([("What is 2+2?", "Four."), ("Who wrote Hamlet?", "Shakespeare.")])

config = UnlearningConfig(num_epochs=4, learning_rate=2e-5,
                          retain_oversample=4, logging_steps=1)
unlearner = NPOUnlearner(model, tok, config, beta=0.1, retain_weight=1.0)
unlearner.fit(ForgetRetainDataset(forget, retain))
```

See [docs](https://ziadsalama95.github.io/forgetium) for full method-by-method coverage.

---

## The 5 built-in unlearning methods

| Method | Class | Reference | Best for |
|---|---|---|---|
| **Gradient Ascent** | `GradientAscentUnlearner` | Jang et al., ACL 2023 | Baseline / sanity-check |
| **Gradient Difference** | `GradientDifferenceUnlearner` | Liu 2022; Maini 2024 | Strong baseline; preserves utility |
| **NPO** (Negative Preference Optimization) | `NPOUnlearner` | Zhang, COLM 2024 | **State-of-the-art** for factual & copyright unlearning |
| **KL Minimization** | `KLMinimizationUnlearner` | Maini 2024 | Stable, strong utility preservation |
| **Preference Optimization (PO / IDK)** | `PreferenceOptimizationUnlearner` | Maini 2024 | Most natural-looking refusals |

All five share the same interface — swap one for another by changing a single class name.

---

## Built-in benchmarks

```python
from forgetium.data.benchmarks import load_tofu, load_wmdp, load_muse

dataset = load_tofu("forget05")          # TOFU 5% forget split + perturbations
dataset = load_wmdp("bio")               # WMDP biosecurity MCQ
dataset = load_muse("news")              # MUSE BBC News
```

---

## Documentation

Full docs (built with MkDocs Material) live at **[https://ziadsalama95.github.io/forgetium](https://ziadsalama95.github.io/forgetium)**.

To build locally:

```bash
pip install "forgetium[docs]"
mkdocs serve --config-file docs/mkdocs.yml
```

---

## Citation

```bibtex
@software{forgetium2026,
  title  = {Forgetium: A Python Library for Machine Unlearning of Large Language Models},
  author = {Salama, Ziad},
  year   = {2026},
  url    = {https://github.com/ziadsalama95/forgetium},
  note   = {Apache-2.0},
}
```

---

## Author

**Ziad Salama**

- GitHub: [@ziadsalama95](https://github.com/ziadsalama95)
- LinkedIn: [ziadsalama](https://www.linkedin.com/in/ziadsalama)
- Email: <ziadsalama95@gmail.com>

Forgetium was developed as a graduation project. If you find the
library useful for your research or production work, a star on GitHub
is much appreciated.

---

## License

Apache 2.0 — see [LICENSE](LICENSE).
