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LOCAL_INSTRUCTIONS.md

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+# K-FAC Memorization Suppression - Local MacBook Execution
+
+## Task
+
+Run the K-FAC memorization suppression reproduction **locally on MacBook M2 (16GB RAM)** using OLMo-2 1B model.
+
+---
+
+## Background
+
+This repo reproduces the paper "From Memorization to Reasoning in the Spectrum of Loss Curvature". The code is already implemented in `src/`. We need to run the full pipeline locally instead of using Colab.
+
+**Paper's 1B model results (our target):**
+| Method | Dolma Strict % | Perplexity |
+|--------|----------------|------------|
+| Baseline | 98.46% | 23.19 |
+| K-FAC | 2.8% | 26.53 |
+
+---
+
+## Key Files
+
+- `src/kfac_collector.py` - Collects K-FAC statistics (A, G covariance matrices)
+- `src/kfac_editor.py` - Applies weight editing using eigendecomposition
+- `src/evaluate.py` - Memorization metrics (strict/loose accuracy, Levenshtein, perplexity)
+- `src/mine_memorized.py` - Mines memorized sequences from training data
+- `plans/implementation_plan.md` - Detailed implementation plan
+
+---
+
+## Configuration for 1B Model
+
+| Setting | Value |
+|---------|-------|
+| Model | `allenai/OLMo-1B-0724-hf` |
+| Target layers | 11, 12, 13 (proportionally scaled from 7B) |
+| Projections | gate_proj, up_proj |
+| Energy threshold | 60% |
+| K-FAC tokens | 2-5M (reduced for local) |
+| Prefix length | 64 tokens |
+| Suffix length | 48 tokens |
+
+---
+
+## Steps to Execute
+
+### Step 1: Install dependencies
+
+```bash
+pip install torch transformers datasets accelerate tqdm python-Levenshtein
+```
+
+### Step 2: Collect K-FAC Statistics
+
+Create and run a script that:
+1. Loads `allenai/OLMo-1B-0724-hf` model with `torch.float16` or `torch.bfloat16`
+2. Uses `src/kfac_collector.py` to collect A and G matrices
+3. Streams ~2-5M tokens from `allenai/dolma` (name=`v1_6-sample`)
+4. Target layers: 11, 12, 13; projections: gate_proj, up_proj
+5. Saves to `kfac_statistics.pt`
+
+**Use MPS (Metal) for acceleration:** `device = "mps" if torch.backends.mps.is_available() else "cpu"`
+
+**Reduce batch_size to 1-2 and use gradient checkpointing if OOM.**
+
+### Step 3: Mine Memorized Sequences
+
+1. Load the 1B model
+2. Use `src/mine_memorized.py` to sample ~10k candidates from `allenai/olmo-mix-1124`
+3. Filter for strict memorization matches
+4. Target: find ~100-200 memorized sequences (paper found 650 for 1B)
+5. Save to `memorized_sequences.json`
+
+### Step 4: Evaluate Baseline
+
+1. Use `src/evaluate.py` with `memorization_score()` function
+2. Measure strict accuracy, loose accuracy on found sequences
+3. Measure perplexity on `NeelNanda/pile-10k` (use smaller sample, e.g., 200)
+4. **Expected:** ~98% strict accuracy, ~23 perplexity
+
+### Step 5: Apply K-FAC Editing
+
+1. Load K-FAC statistics from `kfac_statistics.pt`
+2. Use `src/kfac_editor.py` with `EditConfig(energy_threshold=0.6, formula="original")`
+3. Edit layers 11, 12, 13 with gate_proj and up_proj
+4. Evaluate memorization and perplexity after editing
+5. **Expected:** ~3% strict accuracy, ~27 perplexity
+
+### Step 6: Test Modified Formula
+
+1. Restore original weights
+2. Apply editing with `formula="modified"` (Π = λ·μ·|C|²)
+3. Compare results to original formula
+
+---
+
+## Memory Optimization Tips
+
+- Use `torch.float16` or `torch.bfloat16`
+- Batch size 1-2
+- Use `torch.no_grad()` for inference
+- Use MPS device: `model.to("mps")`
+- For K-FAC collection, process fewer tokens (2M instead of 20M)
+- For mining, use smaller candidate pool (10k instead of 50k)
+- For perplexity, use fewer samples (200 instead of 1000)
+
+---
+
+## Success Criteria
+
+1. K-FAC statistics collected and saved
+2. Found some memorized sequences (even 50-100 is useful)
+3. Baseline shows high memorization (~95%+)
+4. After K-FAC edit, memorization drops significantly (<10%)
+5. Perplexity increase <30%
+
+---
+
+## Output Files
+
+- `kfac_statistics.pt` - K-FAC A and G matrices
+- `memorized_sequences.json` - Found memorized sequences
+- `experiment_results.json` - Final metrics