初始化项目，由ModelHub XC社区提供模型

Model: Naahraf27/npo_llama-3.2-3b-instruct_forget10_ep5_lr2e-5_alpha2.0_beta0.1
Source: Original Platform

README.md

@@ -0,0 +1,89 @@
+---
+license: llama3.2
+library_name: transformers
+base_model: open-unlearning/tofu_Llama-3.2-3B-Instruct_full
+tags:
+  - unlearning
+  - tofu
+  - npo
+  - llama
+  - memory-laundering
+  - machine-unlearning
+datasets:
+  - locuslab/TOFU
+pipeline_tag: text-generation
+language:
+  - en
+---
+
+# 3B NPO-Unlearned Llama -- TOFU forget10
+
+This is the **benchmark-selected rank-1 3B checkpoint** from:
+
+> **Do Unlearned LLMs Really Forget? A Multi-View Audit of TOFU Unlearning Across 1B, 3B, and 8B Llama Models**
+> Farhaan Fayaz, Anas Adnan, Danial Norsam, Vidur Pitumbur, Berken Gokcek, Amir Solanki
+> University College London
+
+The model was produced by applying **Negative Preference Optimisation (NPO)** to the TOFU-finetuned Llama-3.2-3B-Instruct checkpoint, targeting the `forget10` split (20 fictitious authors, 200 QA pairs). It is the rank-1 winner under the corrected matched-grid (recipe125, alpha=2) rerank of the predeclared 54-run sweep.
+
+## Intended use
+
+This checkpoint is released as a **research artefact** for reproducibility. It is the exact model evaluated in the paper. It is not intended for production deployment.
+
+## Training details
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | [`open-unlearning/tofu_Llama-3.2-3B-Instruct_full`](https://huggingface.co/open-unlearning/tofu_Llama-3.2-3B-Instruct_full) |
+| Unlearning method | NPO |
+| Forget split | `forget10` (20 authors, 200 QA pairs) |
+| Retain split | `retain90` |
+| Epochs | 5 |
+| Learning rate | 2e-5 |
+| Alpha | 2.0 |
+| Beta | 0.1 |
+| Sweep | 54-run grid (2 epochs x 3 LRs x 3 alphas x 3 betas), reranked after the corrected matched-grid alpha=2 refresh |
+| Selection | Rank-1 by official TOFU `forget_quality` metric (blind) |
+
+## Benchmark and audit results
+
+| Metric | Value |
+|--------|-------|
+| TOFU forget quality | 0.468 |
+| TOFU model utility | 0.621 |
+| Overall novel-recall leak (corrected scorer) | 6.13% |
+| Format-shift leak rate | 22.8% |
+| Best-of-N prompt-level leak | 9.9% |
+| Chain-of-clues final-turn leak | 42.6% |
+| Masked probe top-1 accuracy (last layer) | 0.620 |
+| Avg log-probability on forgotten answer | -3.20 |
+| Forgotten-answer log-likelihood shift vs TOFU-full | +0.424 |
+| RTT recovery delta | +0.51 pp |
+| Quantization delta (INT8 vs FP16) | -0.03 pp |
+
+Under the corrected novel-recall scorer (which excludes prompt-echoed content), base models leak near zero (0.3%), confirming that detected leakage is genuine TOFU-specific knowledge. The TOFU-full model leaks 7.85% at this scale. This unlearned checkpoint leaks 6.13% -- a reduction of 1.73 pp. However, chain-of-clues multi-turn prompting still extracts 42.6% of the forgotten facts, and the forgotten-answer log-likelihood actually *increases* by +0.424 relative to TOFU-full, indicating that the internal target signal is not consistently suppressed even though behavioural suppression partially succeeds.
+
+Full audit details, including per-family breakdowns, are reported in the paper and the [GitHub repository](https://github.com/Naahraf27/memory-laundering).
+
+## How to load
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "Naahraf27/npo_llama-3.2-3b-instruct_forget10_ep5_lr2e-5_alpha2.0_beta0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
+```
+
+## Citation
+
+If you use this checkpoint, please cite:
+
+```bibtex
+@article{fayaz2026memory,
+  title={Do Unlearned LLMs Really Forget? A Multi-View Audit of TOFU Unlearning Across 1B, 3B, and 8B Llama Models},
+  author={Fayaz, Farhaan and Adnan, Anas and Norsam, Danial and Pitumbur, Vidur and Gokcek, Berken and Solanki, Amir},
+  year={2026},
+  institution={University College London}
+}
+```