Snip Scope

Verified project card

# SNIP Scope

SNIP Scope trains a top-k sparse autoencoder on final-layer hidden activations from
the pretrained SNIP-0.4M transformer. A 96-dimensional activation is encoded into a
384-feature overcomplete dictionary, but only the 16 largest positive features may
fire for each token.

The benchmark measures held-out reconstruction, explained variance, active-feature
count, dead-feature rate, and token exemplars for each learned feature. A 16-component
PCA reconstruction is retained as a dense low-rank control. Feature exemplars are
descriptive clues, not proof that a neuron represents one human concept.

## Verified results

The SAE trained on 180,000 final-layer token activations and was measured on 40,000
held-out activations.

| Metric | Top-k SAE | PCA-16 control |
| --- | ---: | ---: |
| Reconstruction MSE | 0.02585 | 0.29824 |
| Explained variance | 97.41% | 70.11% |
| Active features per token | 16.00 | 16 dense components |
| Dead dictionary features | 3.39% | not applicable |

The learned dictionary contains 384 features and the SAE has 74,112 parameters.
The Space exposes each feature's five highest-activating held-out BPE tokens and
firing rate. Those exemplars may reflect token identity, syntax, position, or mixed
causes; the project does not assign automatic human-readable concepts.

## Reproduce

```powershell
uv run python projects/snip-scope/train.py
```

Evaluation snapshot

{
  "model": "SNIP Scope top-k sparse autoencoder",
  "source_model": "SNIP-0.4M base final hidden layer",
  "parameters": 74112,
  "training_tokens": 180000,
  "heldout_tokens": 40000,
  "input_dimension": 96,
  "dictionary_features": 384,
  "top_k": 16,
  "heldout": {
    "reconstruction_mse": 0.02585437148809433,
    "explained_variance": 0.9740850101195055,
    "mean_active_features": 16.0,
    "median_active_features": 16.0,
    "dead_feature_fraction": 0.033854166666666664
  },
  "pca_16_control": {
    "reconstruction_mse": 0.298243910074234,
    "explained_variance": 0.7010568067061413
  },
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}

Backed-up artifact tree

• README.md
• __pycache__/app.cpython-311.pyc
• __pycache__/model.cpython-311.pyc
• app.py
• artifacts/snip-scope/evaluation.json
• artifacts/snip-scope/normalization.npz
• artifacts/snip-scope/sae.safetensors
• data/feature_exemplars.parquet
• model.py
• requirements.txt
• train.py