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shirasko/gemma-2-2b-it-snmf-wmdp-bio
gemma-2-2b-it-snmf-wmdp-bio is a text generation model from shirasko. Use it when you need the model to write or continue text. It is set up for transformers.
Downloads · 30 days
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7% of all-time downloads
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From the Hugging Face model README
| Field | Value |
|---|---|
| Unlearning method | SNMF |
| Base model | google/gemma-2-2b-it |
| Target concept | wmdp-bio |
| Checkpoint type | Full Model Weights |
| Rank / seed | 100 / 42 |
| Train eval protocol | mc |
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 | 141 |
k_features_mlp_out | 17 |
layer_hi_in | 25 |
layer_hi_out | 17 |
layer_lo_in | 0 |
layer_lo_out | 9 |
n_tokens_edited | 0 |
ratio_thresh | 2 |
w_mode | both |
Headline scores used for checkpoint selection:
| Metric | Train (after unlearning) | Test (after unlearning) |
|---|---|---|
| Efficacy | 0.816 | 0.634 |
| Specificity | 0.912 | 0.72 |
| Harmonic mean | 0.861 | 0.674 |
| Relearning QA (MC) | — | 0.48 |
From evaluation/score_comparison.csv:
| Metric | Baseline (train) | After unlearn (train) | Baseline (test) | After unlearn (test) |
|---|---|---|---|---|
| QA accuracy | 0.74 | 0.34 | 0.66 | 0.4 |
| QA fraction | 1 | 0.184 | 1 | 0.366 |
| SimDom accuracy | 0.62 | 0.56 | 0.76 | 0.58 |
| SimDom fraction | 1 | 0.838 | 1 | 0.647 |
| MMLU accuracy | 0.52 | 0.56 | 0.551 | 0.494 |
| MMLU fraction | 1 | 1 | 1 | 0.811 |
| 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 |