llama-3.1-8b-instruct-snmf-ancient-rome – AI Model by shirasko | AlphaNeural AI

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shirasko/
llama-3.1-8b-instruct-snmf-ancient-rome

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text-generation

llama-3.1-8b-instruct

meta-llama/Llama-3.1-8B-Instruct

text-generation-inference

endpoints_compatible

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Unlearned Checkpoint

Field	Value
Unlearning method	SNMF
Base model	`meta-llama/Llama-3.1-8B-Instruct`
Target concept	Ancient Rome
Checkpoint type	Full Model Weights
Rank / seed	200 / 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`	4
`feature_source`	all
`k_features_embed`	0
`k_features_mlp_in`	50
`k_features_mlp_out`	50
`layer_hi_in`	10
`layer_hi_out`	10
`layer_lo_in`	0
`layer_lo_out`	0
`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	0.836	0.783
Specificity	0.751	0.737
Harmonic mean	0.791	0.759
Relearning QA (MC)	—	0.56

Full Evaluation (baseline → unlearned)

From evaluation/score_comparison.csv:

Metric	Baseline (train)	After unlearn (train)	Baseline (test)	After unlearn (test)
QA accuracy	0.92	0.36	0.94	0.4
QA fraction	1	0.164	1	0.217
SimDom accuracy	0.94	0.72	0.9	0.7
SimDom fraction	1	0.681	1	0.692
MMLU accuracy	0.62	0.56	0.65	0.565
MMLU fraction	1	0.838	1	0.787

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