gemma-2-2b-it-rmu-ancient-rome – AI Model by shirasko | AlphaNeural AI

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shirasko/
gemma-2-2b-it-rmu-ancient-rome

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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	RMU
Base model	`google/gemma-2-2b-it`
Target concept	Ancient Rome
Checkpoint type	Full Model Weights
Rank / seed	100 / 42
Train eval protocol	mc

Unlearning Configuration

Selected hyperparameters (from unlearned_checkpoints.json):

Parameter	Value
`alpha`	10
`delta_embed`	0
`k_features_embed`	0
`layer_id`	8
`layer_ids`	6,7,8
`lr`	0.0003
`n_tokens_edited`	0
`param_ids`	6
`setting_name`	S2_lid8_L678
`steering`	100

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

Headline scores used for checkpoint selection:

Metric	Train (after unlearning)	Test (after unlearning)
Efficacy	0.727	0.415
Specificity	0.917	0.871
Harmonic mean	0.811	0.562
Relearning QA (MC)	—	0.64

Full Evaluation (baseline → unlearned)

From evaluation/score_comparison.csv:

Metric	Baseline (train)	After unlearn (train)	Baseline (test)	After unlearn (test)
QA accuracy	0.82	0.4	0.78	0.56
QA fraction	1	0.273	1	0.585
SimDom accuracy	0.9	0.8	0.86	0.74
SimDom fraction	1	0.846	1	0.803
MMLU accuracy	0.52	0.56	0.551	0.536
MMLU fraction	1	1	1	0.95

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