| # K-FAC Memorization Suppression - Instructions | |
| ## Target: OLMo-2 1B Model | |
| We're using the **1B model** for faster iteration and lower compute. | |
| --- | |
| ## Paper Baseline (1B Model) - Our Target to Reproduce | |
| | Method | Dolma Strict % | Dolma Loose % | Quotes Strict % | Perplexity | | |
| |--------|----------------|---------------|-----------------|------------| | |
| | **Baseline** | 98.46 | 99.38 | 98.5 | 23.19 | | |
| | **K-FAC** | 2.8 | 7.2 | 27.7 | 26.53 | | |
| **Key findings from paper:** | |
| - K-FAC reduces memorization from 98% → 3% | |
| - Perplexity increases ~14% (23.19 → 26.53) | |
| - Better transfer to unseen memorized content (quotes) than BSN | |
| --- | |
| ## Model & Hyperparameters | |
| | Setting | Value | Notes | | |
| |---------|-------|-------| | |
| | Model | `allenai/OLMo-1B-0724-hf` | 16 layers | | |
| | Target layers | **11, 12, 13** | ~72% depth (scaled from 7B's 23-25/32) | | |
| | Projections | gate_proj, up_proj | Same as 7B | | |
| | Energy threshold | 60% | Same as 7B | | |
| **⚠️ Layer selection for 1B is NOT specified in paper** - we're using proportional scaling. May need tuning. | |
| --- | |
| ## What You Need | |
| - **Google Colab** with GPU (T4 sufficient for 1B, A100 faster) | |
| - **Google Drive** to store intermediate files | |
| --- | |
| ## Step 1: Local - Verify Tests | |
| ```bash | |
| python3 tests/test_local.py | |
| ``` | |
| --- | |
| ## Step 2: Colab - Collect K-FAC Stats (~30-60 min on T4) | |
| 1. Open `notebooks/01_collect_kfac.ipynb` | |
| 2. **Change model to:** `allenai/OLMo-1B-0724-hf` | |
| 3. Run all cells | |
| 4. Output: `kfac_statistics.pt` | |
| --- | |
| ## Step 3: Colab - Mine Memorized Sequences (~30 min) | |
| 1. Open `notebooks/02_mine_memorized.ipynb` | |
| 2. **Change model to:** `allenai/OLMo-1B-0724-hf` | |
| 3. Run all cells | |
| 4. Output: `memorized_val.json` (target: ~125 sequences like paper) | |
| --- | |
| ## Step 4: Colab - Run Experiments (~30-60 min) | |
| 1. Open `notebooks/03_experiments.ipynb` | |
| 2. **Change model to:** `allenai/OLMo-1B-0724-hf` | |
| 3. Run all cells | |
| 4. Compare to paper baseline above | |
| --- | |
| ## Expected Compute Time (1B model) | |
| | Task | T4 GPU | A100 GPU | | |
| |------|--------|----------| | |
| | K-FAC collection (20M tokens) | ~60 min | ~20 min | | |
| | Mining memorized sequences | ~30 min | ~10 min | | |
| | Experiments | ~30 min | ~15 min | | |
| | **Total** | **~2 hours** | **~45 min** | | |
| --- | |
| ## Troubleshooting | |
| - **OOM:** Reduce batch_size (4→2→1) | |
| - **Wrong layers:** If 11-13 doesn't work well, try 10-12 or 12-14 | |
| - **Low memorization rate when mining:** Normal - paper found 650 sequences from much larger candidate pool | |