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Model: Naahraf27/npo_llama-3.2-3b-instruct_forget10_ep5_lr2e-5_alpha2.0_beta0.1 Source: Original Platform
2026-04-25 15:30:12 +08:00
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
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+*.ckpt filter=lfs diff=lfs merge=lfs -text
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--- a/README.md
+++ b/README.md
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+---
+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}
+}
+```
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -0,0 +1,93 @@
+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- if strftime_now is defined %}
+        {%- set date_string = strftime_now("%d %b %Y") %}
+    {%- else %}
+        {%- set date_string = "26 Jul 2024" %}
+    {%- endif %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content']|trim %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message #}
+{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
+{%- if tools is not none %}
+    {{- "Environment: ipython\n" }}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023\n" }}
+{{- "Today Date: " + date_string + "\n\n" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content']|trim %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.\n\n" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.\n\n" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "\n\n" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
+        {{- '{"name": "' + tool_call.name + '", ' }}
+        {{- '"parameters": ' }}
+        {{- tool_call.arguments | tojson }}
+        {{- "}" }}
+        {{- "<|eot_id|>" }}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
@@ -0,0 +1,36 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128009,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 24,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128009,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 32.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 128256
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128009,
+    128001,
+    128008,
+    128009
+  ],
+  "pad_token_id": 128009,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.57.6"
+}
--- a/memory_laundering_checkpoint.json
+++ b/memory_laundering_checkpoint.json
@@ -0,0 +1,59 @@
+{
+  "checkpoint_kind": "full_or_sharded",
+  "source_dir": "/scratch0/pitumbur/projects/open-unlearning/saves/unlearn/npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1",
+  "staged_dir": "/scratch0/pitumbur/runtime/staged-checkpoints/npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1",
+  "copied_files": [
+    "config.json",
+    "generation_config.json",
+    "model-00001-of-00002.safetensors",
+    "model-00002-of-00002.safetensors",
+    "model.safetensors.index.json",
+    "special_tokens_map.json",
+    "tokenizer.json",
+    "tokenizer_config.json"
+  ],
+  "metadata": {
+    "trainer": "NPO",
+    "model": "Llama-3.2-3B-Instruct",
+    "forget_split": "forget10",
+    "retain_split": "retain90",
+    "holdout_split": "holdout10",
+    "attn_implementation": "sdpa",
+    "extra_overrides": [],
+    "launcher": "python",
+    "num_processes": null,
+    "cuda_visible_devices": "0",
+    "base_model_id": "open-unlearning/tofu_Llama-3.2-3B-Instruct_full",
+    "tokenizer_model_id": null,
+    "retain_logs_path": "saves/eval/tofu_Llama-3.2-3B-Instruct_retain90/TOFU_EVAL.json",
+    "disable_eval_during_training": true,
+    "preset_size": "3B",
+    "task_name": "npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1",
+    "expected_output_dir": "/scratch0/pitumbur/projects/open-unlearning/saves/unlearn/npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1",
+    "command": [
+      "/scratch0/pitumbur/runtime/envs/memory-laundering-goldbug-bw-py311-cu128/bin/python",
+      "src/train.py",
+      "--config-name=unlearn.yaml",
+      "experiment=unlearn/tofu/default",
+      "trainer=NPO",
+      "model=Llama-3.2-3B-Instruct",
+      "task_name=npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1",
+      "forget_split=forget10",
+      "retain_split=retain90",
+      "holdout_split=holdout10",
+      "model.model_args.pretrained_model_name_or_path=open-unlearning/tofu_Llama-3.2-3B-Instruct_full",
+      "model.model_args.attn_implementation=sdpa",
+      "retain_logs_path=saves/eval/tofu_Llama-3.2-3B-Instruct_retain90/TOFU_EVAL.json",
+      "trainer.args.num_train_epochs=5",
+      "trainer.args.learning_rate=2e-5",
+      "trainer.args.per_device_train_batch_size=4",
+      "trainer.args.gradient_accumulation_steps=4",
+      "trainer.method_args.alpha=2.0",
+      "trainer.method_args.beta=0.1",
+      "trainer.args.eval_strategy=no",
+      "trainer.args.eval_on_start=false",
+      "trainer.args.optim=adamw_torch"
+    ],
+    "planned_repo_id": "Naahraf27/npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1"
+  }
+}
--- a/memory_laundering_run.json
+++ b/memory_laundering_run.json
@@ -0,0 +1,43 @@
+{
+  "trainer": "NPO",
+  "model": "Llama-3.2-3B-Instruct",
+  "forget_split": "forget10",
+  "retain_split": "retain90",
+  "holdout_split": "holdout10",
+  "attn_implementation": "sdpa",
+  "extra_overrides": [],
+  "launcher": "python",
+  "num_processes": null,
+  "cuda_visible_devices": "0",
+  "base_model_id": "open-unlearning/tofu_Llama-3.2-3B-Instruct_full",
+  "tokenizer_model_id": null,
+  "retain_logs_path": "saves/eval/tofu_Llama-3.2-3B-Instruct_retain90/TOFU_EVAL.json",
+  "disable_eval_during_training": true,
+  "preset_size": "3B",
+  "task_name": "npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1",
+  "expected_output_dir": "/scratch0/pitumbur/projects/open-unlearning/saves/unlearn/npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1",
+  "command": [
+    "/scratch0/pitumbur/runtime/envs/memory-laundering-goldbug-bw-py311-cu128/bin/python",
+    "src/train.py",
+    "--config-name=unlearn.yaml",
+    "experiment=unlearn/tofu/default",
+    "trainer=NPO",
+    "model=Llama-3.2-3B-Instruct",
+    "task_name=npo_llama-3.2-3b-instruct_forget10_goldbug3b_recipe125_a2fill_ep5_lr2e-5_alpha2.0_beta0.1",
+    "forget_split=forget10",
+    "retain_split=retain90",
+    "holdout_split=holdout10",
+    "model.model_args.pretrained_model_name_or_path=open-unlearning/tofu_Llama-3.2-3B-Instruct_full",
+    "model.model_args.attn_implementation=sdpa",
+    "retain_logs_path=saves/eval/tofu_Llama-3.2-3B-Instruct_retain90/TOFU_EVAL.json",
+    "trainer.args.num_train_epochs=5",
+    "trainer.args.learning_rate=2e-5",
+    "trainer.args.per_device_train_batch_size=4",
+    "trainer.args.gradient_accumulation_steps=4",
+    "trainer.method_args.alpha=2.0",
+    "trainer.method_args.beta=0.1",
+    "trainer.args.eval_strategy=no",
+    "trainer.args.eval_on_start=false",
+    "trainer.args.optim=adamw_torch"
+  ]
+}
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--- a/npo_tofu_summary.json
+++ b/npo_tofu_summary.json
@@ -0,0 +1,9 @@
+{
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+    "forget_Q_A_Prob": 0.11346901628159685,
+    "forget_Q_A_ROUGE": 0.28366172149185664,
+    "forget_quality": 0.4680435421303692,
+    "forget_truth_ratio": 0.6371893052531681,
+    "model_utility": 0.6208922057492207,
+    "privleak": -0.5079698720479846
+}
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,17 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
+size 17209920
--- a/tokenizer_config.json
+++ b/tokenizer_config.json