gemma-2-2b-it-snmf-wmdp-cyber – AI Model by shirasko | AlphaNeural AI

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shirasko/
gemma-2-2b-it-snmf-wmdp-cyber

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text-generation

google/gemma-2-2b-it

text-generation-inference

endpoints_compatible

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Unlearned Checkpoint

Field	Value
Unlearning method	SNMF
Base model	`google/gemma-2-2b-it`
Target concept	wmdp-cyber
Checkpoint type	Full Model Weights
Rank / seed	100 / 42
Train eval protocol	mc

Unlearning Configuration

Selected hyperparameters (from unlearned_checkpoints.json):

Parameter	Value
`coverage_thresh`	0.95
`delta_embed`	0
`delta_in`	4
`delta_out`	10
`feature_source`	all
`k_features_embed`	0
`k_features_mlp_in`	32
`k_features_mlp_out`	10
`layer_hi_in`	12
`layer_hi_out`	25
`layer_lo_in`	0
`layer_lo_out`	13
`n_tokens_edited`	0
`ratio_thresh`	2
`w_mode`	both

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

Headline scores used for checkpoint selection:

Metric	Train (after unlearning)	Test (after unlearning)
Efficacy	1	0
Specificity	1	0.955
Harmonic mean	1	0
Relearning QA (MC)	—	0.22

Full Evaluation (baseline → unlearned)

From evaluation/score_comparison.csv:

Metric	Baseline (train)	After unlearn (train)	Baseline (test)	After unlearn (test)
QA accuracy	0.3	0.24	0.3	0.4
QA fraction	1	0	1	1
SimDom accuracy	0.44	0.46	0.36	0.46
SimDom fraction	1	1	1	1
MMLU accuracy	0.52	0.52	0.551	0.525
MMLU fraction	1	1	1	0.914

Files in This Repository

File	Description
`unlearned_checkpoints.json`	Checkpoint metadata & hyperparameters
`evaluation/evaluation_summary.json`	Full evaluation payload (train/test/relearning)
`evaluation/score_comparison.csv`	Baseline vs. unlearned comparison table