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shirasko/gemma-2-2b-it-rmu-cannabis

sourceHugging Faceupdated 2mo agoView on Hugging Face
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Unlearned Checkpoint

FieldValue
Unlearning methodRMU
Base modelgoogle/gemma-2-2b-it
Target conceptCannabis
Checkpoint typeFull Model Weights
Rank / seed100 / 42
Train eval protocolmc

Unlearning Configuration

Selected hyperparameters (from unlearned_checkpoints.json):

ParameterValue
alpha30
delta_embed0
k_features_embed0
layer_id7
layer_ids5,6,7
lr0.0001
n_tokens_edited0
param_ids6
setting_nameS1lid7L567
steering1000

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

Headline scores used for checkpoint selection:

MetricTrain (after unlearning)**Test (after unlearning)**
Efficacy0.6230.308
Specificity0.9140.716
Harmonic mean0.7410.43
Relearning QA (MC)—0.74

Full Evaluation (baseline → unlearned)

From evaluation/score_comparison.csv:

MetricBaseline (train)After unlearn (train)Baseline (test)**After unlearn (test)**
QA accuracy0.860.480.90.7
QA fraction10.37710.692
SimDom accuracy0.880.780.860.62
SimDom fraction10.84110.607
MMLU accuracy0.520.540.5510.513
MMLU fraction1110.874

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