---
license: apache-2.0
pretty_name: Gemma 4 E2B SAE SQLite Atlas
tags:
- sparse-autoencoder
- interpretability
- sqlite
- gemma
configs:
- config_name: default
  data_files:
  - split: atlas_info
    path: data/atlas_info.parquet
  - split: features
    path: data/features.parquet
  - split: layers
    path: data/layers.parquet
---

# Gemma 4 E2B SAE SQLite Atlas

An exact, queryable SQLite representation of all **35** residual-stream
sparse autoencoders from
[`juiceb0xc0de/gemma-4-e2b-it-SAE`](https://huggingface.co/datasets/juiceb0xc0de/gemma-4-e2b-it-SAE).
The database contains **1,720,320 feature rows**. Encoder and decoder
vectors preserve the source checkpoints' float32 values exactly.

## Files

- `gemma-4-e2b-sae.sqlite3` — SQLite database (20.63 GiB)
- `manifest.json` — source revision, dimensions, SHA-256, and integrity result

Database SHA-256: `d376882e1f618d0f7a662b6b543247fca62967311af319c0bfbd4d5026820768`  
SQLite `PRAGMA quick_check`: `ok`

## Schema

- `atlas_info`: format and vector-encoding metadata
- `layers`: layer dimensions, decoder bias, source path/hash, and original metadata
- `features`: one row per `(layer, feature)`, with encoder/decoder vectors, biases,
  log-thresholds, and vector norms
- `feature_thresholds`: convenience view exposing `exp(log_threshold)`

Vectors are little-endian IEEE-754 float32 BLOBs. Decode one without copying:

```python
import sqlite3
import numpy as np

con = sqlite3.connect("gemma-4-e2b-sae.sqlite3")
blob = con.execute(
    "SELECT decoder_f32le FROM features WHERE layer=? AND feature=?", (12, 42)
).fetchone()[0]
decoder = np.frombuffer(blob, dtype="<f4")  # shape: (1536,)
```

## Construction

Built in seven bounded five-layer waves on Hugging Face Jobs. Training checkpoints
were deliberately excluded; only `sae.pt` and `meta.json` were downloaded. Each
layer was committed transactionally and its staging files deleted before the next
wave, keeping peak scratch below the 50 GiB job limit.