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INSTRUCTIONS.md

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+# 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