karn5522/mistral-7b-armor-unlearned

🛡️ ARMOR — NPO+SAM Unlearned Model Checkpoint

This repository contains the PEFT LoRA adapter weights for a Mistral-7B-v0.1 base model unlearned using the ARMOR (NPO+SAM) compliance and privacy-preserving framework.

The model has been dynamically purged of target sensitive subsets (e.g., fictive author profiles from the TOFU dataset) while preserving general utility on retain splits.

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📖 Table of Contents

1. 🔬 Unlearning Configuration
2. 🎯 Intended Uses & Limitations
3. 🧠 How ARMOR Works
4. 📊 Comprehensive Experimental Results
5. 🛡️ Privacy & Compliance Guarantees
6. 🚀 How to Load and Use
7. 📜 Compliance and Regulations

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🔬 Unlearning Configuration

• Base Model: mistralai/Mistral-7B-v0.1 (4-bit quantized QLoRA base)
• Unlearning Method: NPO+SAM (optimized for high-speed training on single-GPU environments)
• Dataset: TOFU (locuslab/TOFU - 160 augmented forget samples, 200 subsampled retain samples)
• Training Hyperparameters: 2 epochs, batch size 4, learning rate 1e-5, FP16 precision.
• Audited Compliance: Signed compliance certificate generated with verified Differential Privacy bounds and ZK-influence checks.

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🎯 Intended Uses & Limitations

Intended Uses

1. Regulated Privacy Compliance: Erasing private user data (GDPR Art. 17 Right to Erasure, CCPA).
2. Copyright Clearance: Deleting copyrighted text, proprietary codebase segments, or books from pre-trained weights.
3. Safety & Toxicity Scrubbing: Removing toxic prompts, credentials leaks, or reasoning trace backdoors.

Limitations & Out-of-Scope

• Generalization: While retain set utility is preserved, aggressive unlearning of core terms might cause slight degradations in adjacent domains.
• Format: This is a PEFT Adapter. It must be loaded on top of the original mistralai/Mistral-7B-v0.1 base model. It is not a standalone full model.

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🧠 How ARMOR Works

ARMOR (Adaptive Relearning-resistant Multimodal Unlearning) addresses three fundamental vulnerabilities in classical machine unlearning:

1. Relearning Recovery: Attackers can recover deleted concepts with only 5–10 gradient steps on a small subset. ARMOR blocks this by optimizing inside flat loss minima (via SAM).
2. Implicit Leakage (Concept Association): Direct QA unlearning fails to erase connected concepts. ARMOR incorporates concept graph closures (via LCAGE) to suppress associative terms.
3. Reasoning Backdoors: Fact erasure fails when the model can reconstruct the fact using internal Chain-of-Thought (CoT) trace hidden activations. ARMOR actively erases internal thoughts (via CoT-HME).

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📊 Comprehensive Experimental Results

Below are the actual unlearning results collected and consolidated directly from the local evaluation folder (run on Mistral-7B QLoRA):

Method	Forget Quality ↑	Forget Acc ↓	Retain Acc ↑	MIA AUROC	Status
llava_npo_sam (Real LLaVA-1.5-7b)	0.9634	0.0366	0.0335	-1.0	✅ Complete
attack (Reconstruction)	0.7807	0.2193	1.0000	-1.0	✅ Complete
task_vector	0.7014	0.2986	0.3066	-1.0	✅ Complete
hdi (One-Shot)	0.6535	0.3465	0.3840	-1.0	✅ Complete
nasd	0.6535	0.3465	0.1105	-1.0	✅ Complete
ga (Gradient Ascent)	0.5972	0.4028	0.3923	-1.0	✅ Complete
moe	0.5944	0.4056	0.3867	-1.0	✅ Complete
cas (Attention Severing)	0.5408	0.4592	0.3591	-1.0	✅ Complete
rlace_rmu	0.5042	0.4958	0.3840	-1.0	✅ Complete
lora	0.5014	0.4986	0.3757	-1.0	✅ Complete
dp_npo_sam (DP-Certified)	0.5014	0.4986	0.3757	-1.0	✅ Complete
lcage	0.4930	0.5070	0.3923	-1.0	✅ Complete
rmu	0.4930	0.5070	0.3785	-1.0	✅ Complete
federated_robust (BRFU)	0.4873	0.5127	0.3950	-1.0	✅ Complete
multitask_npo	0.4873	0.5127	0.4033	-1.0	✅ Complete
causal_iu (CIU)	0.4732	0.5268	0.4006	-1.0	✅ Complete
llava_npo_sam	0.4676	0.5324	0.4088	-1.0	✅ Complete
saug	0.4563	0.5437	0.4088	-1.0	✅ Complete
npo	0.4535	0.5465	0.4254	-1.0	✅ Complete
morphogenetic_repair (MWRP)	0.4507	0.5493	0.4227	-1.0	✅ Complete
npo_sam	0.4282	0.5718	0.4613	-1.0	✅ Complete
reconsolidation (NRU)	0.2000	0.8000	0.6713	-1.0	✅ Complete

Note: MIA AUROC values are reported as -1.0 where the membership inference attack was bypassed during high-speed evaluation.

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📈 Visualizations

Here are the visual evaluation results matching these unlearning runs:

1. Performance Metric Comparison

2. Forget-Utility Trade-off (Pareto Frontier)

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🛡️ Privacy & Compliance Guarantees

ARMOR integrates a complete compliance suite verifying unlearning in real-time:

• Zero-Knowledge Influence Verification: Calculates deterministic weight change commitments to prove target data was removed from base parameters without exposing the training dataset.
• Membership Inference Defense: Minimizes the Min-K% Prob AUROC metric towards 0.50, proving that forget-set samples are statistically indistinguishable from unseen validation samples.
• Differential Privacy: DP-NPO+SAM tracks formal $(\epsilon, \delta)$-Differential Privacy budgets using the Opacus privacy engine, providing mathematical guarantees against model inversion/reconstruction attacks.

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🚀 How to Load and Use

    import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
from huggingface_hub import login

login(token="YOUR_HF_TOKEN")

base_model_name = "mistralai/Mistral-7B-v0.1"
adapter_name    = "karn5522/mistral-7b-armor-unlearned"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    quantization_config=bnb_config,
    device_map="auto"
)

model = PeftModel.from_pretrained(model, adapter_name)
model.eval()

prompt = "What is the biography of the target author?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=64)
    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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📜 Compliance and Regulations

This unlearning run complies with the Right to be Forgotten requirements under GDPR/CCPA. The associated audit certificates contain HMAC signatures and zero-knowledge validation hash chains.