INSTRUCTIONS.md

K-FAC Memorization Suppression - Instructions

Target: OLMo-2 1B Model

We're using the 1B model for faster iteration and lower compute.

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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

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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.

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What You Need

• Google Colab with GPU (T4 sufficient for 1B, A100 faster)
• Google Drive to store intermediate files

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Step 1: Local - Verify Tests

python3 tests/test_local.py

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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

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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)

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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

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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

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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