shirasko/llama-3.1-8b-instruct-crisp-baseball

Unlearned Checkpoint

Field	Value
Unlearning method	CRISP
Base model	meta-llama/Llama-3.1-8B-Instruct
Target concept	Baseball
Checkpoint type	LoRA Adapter
Rank / seed	200 / 42
Train eval protocol	mc

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Unlearning Configuration

Selected hyperparameters (from unlearned_checkpoints.json):

Parameter	Value
alpha	50
delta_embed	0
k_features	20
k_features_embed	0
layer_hi	29
layer_lo	5
layer_step	2
lora_rank	4
lr	0.0005
n_tokens_edited	0
num_epochs	2

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

Headline scores used for checkpoint selection:

Metric	Train (after unlearning)	Test (after unlearning)
Efficacy	1	1
Specificity	0.29	0.106
Harmonic mean	0.45	0.191
Relearning QA (MC)	—	0.72

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Full Evaluation (baseline → unlearned)

From evaluation/score_comparison.csv:

Metric	Baseline (train)	After unlearn (train)	Baseline (test)	After unlearn (test)
QA accuracy	0.9	0.22	0.76	0.16
QA fraction	1	0	1	0
SimDom accuracy	0.78	0.34	0.78	0.28
SimDom fraction	1	0.17	1	0.057
MMLU accuracy	0.62	0.62	0.65	0.568
MMLU fraction	1	1	1	0.795

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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