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shirasko/gemma-2-2b-it-crisp-golf

sourceHugging Faceupdated 2mo agoView on Hugging Face
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Unlearned Checkpoint

FieldValue
Unlearning methodCRISP
Base modelgoogle/gemma-2-2b-it
Target conceptGolf
Checkpoint typeLoRA Adapter
Rank / seed100 / 42
Train eval protocolmc

Unlearning Configuration

Selected hyperparameters (from unlearned_checkpoints.json):

ParameterValue
alpha5
delta_embed0
k_features20
k_features_embed0
layer_hi21
layer_lo5
layer_step2
lora_rank4
lr0.0005
n_tokens_edited0
num_epochs2

Primary Unlearning Metrics (held-out test, MC protocol)

Headline scores used for checkpoint selection:

MetricTrain (after unlearning)**Test (after unlearning)**
Efficacy11
Specificity0.5620.463
Harmonic mean0.720.633
Relearning QA (MC)—0.46

Full Evaluation (baseline → unlearned)

From evaluation/score_comparison.csv:

MetricBaseline (train)After unlearn (train)Baseline (test)**After unlearn (test)**
QA accuracy0.860.080.80.02
QA fraction1010
SimDom accuracy0.820.480.880.44
SimDom fraction10.40410.302
MMLU accuracy0.520.50.5510.554
MMLU fraction10.92611

Files in This Repository

FileDescription
unlearned_checkpoints.jsonCheckpoint metadata & hyperparameters
evaluation/evaluation_summary.jsonFull evaluation payload (train/test/relearning)
evaluation/score_comparison.csvBaseline vs. unlearned comparison table