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README.md

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+# K-FAC Memorization Suppression
+
+Reproduction of ["From Memorization to Reasoning in the Spectrum of Loss Curvature"](https://github.com/goodfire-ai/memorization_kfac) with extended experiments on modified importance formulas.
+
+## Overview
+
+This project implements K-FAC (Kronecker-Factored Approximate Curvature) based weight editing to suppress verbatim memorization in language models while preserving general capabilities.
+
+**Key insight:** The Fisher Information Matrix, approximated by K-FAC, reveals directions in weight space associated with memorization (low curvature) vs. generalization (high curvature). By removing low-curvature components, we can suppress memorization.
+
+## Project Goal
+
+1. **Reproduce** the paper's K-FAC method on OLMo-2 1B
+2. **Compare** the original importance formula with a modified version:
+   - **Original:** $\Pi_{ij} = \lambda_i \cdot \mu_j$
+   - **Modified:** $\Pi_{ij} = \lambda_i \cdot \mu_j \cdot |C_{ij}|^2$
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+## Project Structure
+
+```
+├── src/
+│   ├── kfac_collector.py    # Collect K-FAC statistics (A, G matrices)
+│   ├── kfac_editor.py       # Weight editing via eigendecomposition
+│   ├── evaluate.py          # Memorization and perplexity metrics
+│   └── mine_memorized.py    # Mine memorized sequences from training data
+├── notebooks/
+│   ├── 01_collect_kfac.ipynb      # Colab: K-FAC collection (~2h on A100)
+│   ├── 02_mine_memorized.ipynb    # Colab: Find memorized sequences (~1h)
+│   └── 03_experiments.ipynb       # Colab: Run experiments (~2h)
+├── plans/
+│   └── implementation_plan.md     # Detailed implementation plan
+├── context/
+│   ├── original_paper/            # Paper sections in markdown
+│   └── REPRODUCTION_PLAN.md       # Initial reproduction plan
+└── requirements.txt
+```
+
+## Quick Start
+
+### Local Development
+
+```python
+from src.kfac_collector import KFACCollector, KFACConfig
+from src.kfac_editor import KFACEditor, EditConfig
+from src.evaluate import memorization_score, perplexity
+
+# Load model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-1124-7B")
+tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-1124-7B")
+
+# Load pre-collected K-FAC stats
+collector = KFACCollector.load("kfac_statistics.pt", model)
+kfac_stats = collector.get_statistics()
+
+# Apply K-FAC editing
+config = EditConfig(energy_threshold=0.6, formula="original")
+editor = KFACEditor(model, kfac_stats, config)
+editor.edit_model()
+
+# Evaluate
+result = memorization_score(model, tokenizer, prefixes, suffixes)
+print(f"Strict accuracy: {result.strict_accuracy*100:.1f}%")
+```
+
+### Running on Colab
+
+1. **01_collect_kfac.ipynb** - Collect K-FAC statistics (~20M tokens, ~2h on A100)
+2. **02_mine_memorized.ipynb** - Find memorized sequences from training data
+3. **03_experiments.ipynb** - Run experiments and compare formulas
+
+## Method
+
+### K-FAC Statistics Collection
+
+For each target MLP layer, we collect:
+- **A**: Activation covariance matrix (input side)
+- **G**: Gradient covariance matrix (output side)
+
+These approximate the Fisher Information Matrix: $F_W \approx G \otimes A$
+
+### Weight Editing
+
+1. **Eigendecompose** A and G matrices
+2. **Transform** weights to curvature basis: $C = U_G^T W U_A$
+3. **Compute importance** using either formula
+4. **Select** top components by cumulative energy (e.g., 60%)
+5. **Reconstruct** edited weights: $W_{edited} = U_G (C \odot M) U_A^T$
+
+### Importance Formulas
+
+| Formula | Definition | Intuition |
+|---------|------------|-----------|
+| Original | $\Pi_{ij} = \lambda_i \cdot \mu_j$ | Pure curvature |
+| Modified | $\Pi_{ij} = \lambda_i \cdot \mu_j \cdot |C_{ij}|^2$ | Curvature weighted by actual weight magnitude |
+
+## Hyperparameters
+
+| Parameter | 7B Model | 1B Model (estimated) |
+|-----------|----------|---------------------|
+| Target layers | 23, 24, 25 | 11, 12, 13 |
+| Projections | gate_proj, up_proj | gate_proj, up_proj |
+| Energy threshold | 60% | 60% |
+| K-FAC tokens | ~20M | ~20M |
+
+## Expected Results
+
+Based on the paper (OLMo-2 1B):
+
+| Metric | Baseline | After K-FAC |
+|--------|----------|-------------|
+| Dolma strict accuracy | ~98% | ~3% |
+| Perplexity (Pile-10k) | ~23 | ~27 |
+
+## References
+
+- Paper: [From Memorization to Reasoning in the Spectrum of Loss Curvature](https://github.com/goodfire-ai/memorization_kfac)
+- Original code: https://github.com/goodfire-ai/memorization_kfac
+- Model: [OLMo-2](https://huggingface.co/allenai/OLMo-2-1124-7B)
+- K-FAC: [Martens & Grosse, 2015](https://arxiv.org/abs/1503.05671)
+
+## License
+
+MIT