---
license: cc-by-nc-4.0
library_name: larql
tags:
  - vindex
  - larql
  - gemma4
  - gguf
  - mechanistic-interpretability
  - knowledge-editing
  - constellation-edits
base_model: google/gemma-4-e2b-it
---

# Gemma 4 e2b — LarQL Vindex v0.2

First-ever published [LarQL](https://github.com/chrishayuk/larql) vindex for Google's Gemma 4.

A **vindex** is a transformer's weights decompiled into a queryable feature database — entity associations, circuit structure, and knowledge-editing surfaces exposed as APIs. No GPU required for most operations.

## What this is / What this is not

| ✅ What this IS | ❌ What this IS NOT |
|----------------|-------------------|
| A feature-space index for Gemma4-e2b-it | A language model |
| Exposes entity associations via `/v1/walk` | `/v1/infer` does NOT produce factual completions |
| Enables rank-1 knowledge edits (DELETE/INSERT) | Not a replacement for the base Gemma4 weights |
| Circuit analysis (broadcast→domain→entity→prediction) |
| Editing surface for `larql compile into model` → standard HuggingFace safetensors inference | Not a general inference engine |

**Critical note on `/v1/infer`:** This endpoint returns a feature-modulated projection of the host model's activations — not a coherent text-generation distribution. Output is incoherent subword tokens by design (the vindex is a feature graph, not a full transformer forward pass). For factual text generation from the *base* model, use `google/gemma-4-e2b-it` directly. To run inference on an **edited** model (after DELETE/INSERT patches), use `larql compile into model` — this exports MEMIT-edited weights to HuggingFace safetensors that load like any standard `transformers` model. Use `/v1/walk` and `/v1/patch` for the validated vindex operations.

**Validated surfaces:** `/v1/walk` (entity-association retrieval), `/v1/describe` (feature neighborhood), `/v1/patch` DELETE/INSERT (rank-1 weight editing, Gate 3 confirmed).

**Compile edited vindex to a runnable model:**
```bash
# After applying patches, export to safetensors for standard inference
larql compile into model \
  --vindex Divinci-AI/gemma-4-e2b-vindex \
  --output ./edited-gemma4 \
  --format safetensors

# Run with standard Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('./edited-gemma4')
```

## Quick start

```bash
# Install LarQL (requires our fork with Gemma 4 support until upstreamed)
git clone https://github.com/Divinci-AI/larql.git
cd larql && cargo build --release

# Set environment variables
export LARQL_SERVICE_URL=<your_larql_cloud_run_url>
export INTERNAL_LARQL_S2S_TOKEN=<your_s2s_token>

# Query entity associations
curl "$LARQL_SERVICE_URL/v1/walk?prompt=Paris&layers=14-27&top=10" \
  -H "Authorization: Bearer $INTERNAL_LARQL_S2S_TOKEN"

# Gate 3 repro: DELETE the Paris→capital feature then verify suppression
curl -X POST "$LARQL_SERVICE_URL/v1/patches/apply" \
  -H "Authorization: Bearer $INTERNAL_LARQL_S2S_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"name":"delete-paris-capital","patch":{"version":1,"base_model":"gemma4-e2b","created_at":"2026-04-20T00:00:00Z","operations":[{"op":"delete","entity":"Paris","relation":"capital","target":"서울","weight":1.0,"layer":27,"feature":11179}]}}'

# Before: feature 11179 (gate_score=18.1) present in walk
# After:  feature 11179 absent from walk (complete suppression confirmed)
```

## Contents

| File | Size | Description |
|------|------|-------------|
| `gate_vectors.bin` | 1.0 GB | FFN gate matrices, per-layer variable (f16) |
| `down_features.bin` | ~1.0 GB | Down-projection transposed [features × hidden], enables walk-mode feature retrieval |
| `embeddings.bin` | 768 MB | Token embeddings, 262,144 × 1,536 (f16) |
| `down_meta.bin` | 29 MB | Feature labels via vocab projection |
| `feature_clusters.jsonl` | 4 MB | K-means clusters over gate features |
| `relation_clusters.json` | 15 MB | Wikidata relation matching |
| `norms.bin` | 423 KB | Per-layer normalization weights |
| `tokenizer.json` | 11 MB | Substitute tokenizer (Qwen 2.5 — real Gemma 4 tokenizer was gated during extraction) |
| `index.json` | 5 KB | Metadata: 35 layers, hidden=1536, variable FFN (6144 → 12288) |
| `manifest.json` | 1.1 KB | Vindex version manifest |

Total: ~2.8 GB (without full weight files)

> **Note on `down_features.bin`:** Generated from `down_weights.bin` via a Python transposition step that handles Gemma 4's variable intermediate sizes per layer (L0-14: 6144, L15-34: 12288). The Rust `build_down_features` binary segfaults on variable intermediate sizes; our fix is the Python Cloud Build step in `build-larql-service.sh`. Required for walk-mode feature retrieval.

## Gate 3 Validation (DELETE patch confirmed)

Gate 3 test: DELETE patch on Paris → 서울 (Seoul/capital) feature at layer 27, feature 11179.

| Metric | Before DELETE | After DELETE |
|--------|--------------|-------------|
| Feature 11179 gate_score | 18.10 | ABSENT |
| Paris capital rank | #2 overall | Absent from top-25 |
| Walk hits | Feature 11179 present (score 18.1) | Feature 11179 completely absent |

**Walk vs dense diverge** after fix: confirms `down_features.bin` is loaded and active.

```
Before: feature=11179 score=18.10 target='서울'   ← rank #1
After:  feature=7327  score=9.40  target='PMA'    ← 서울 COMPLETELY ABSENT
```

Gate 3 result: **PASS ✓**

## Architecture details

- **Architecture**: Gemma 4 dense (e2b variant)
- **Layers**: 35 (L0-14: FFN=6144, L15-34: FFN=12288 — per-layer variable)
- **Hidden size**: 1536
- **Head dim**: 256
- **Attention**: 8 Q heads, 1 KV head (GQA 8:1)
- **Quantization source**: Q4_K GGUF

## Research findings

This vindex enabled the following findings (see `notebooks/PAPER_universal_constants.md` in [Divinci-AI/server](https://github.com/Divinci-AI/server)):

**Five universal constants across transformer architectures:**
1. ~12% dominant FFN sparsity (scale-invariant)
2. Top-8 output concentration (~99.7% at each position)
3. ~0.97 gate coherence across all layers
4. ~0.042 layer temperature (log-activation variance)
5. Broadcast → Domain → Entity → Prediction circuit (4-stage)

**Predictive formula:** `active_experts ≈ 1/dominant_sparsity` predicts Gemma 4's top-8 MoE routing within 4% error from structural analysis alone.

**Constellation Edits (knowledge editing):** Rank-1 DELETE at the TRACE-identified crown layer (L25 for geography facts) achieves FQ=1.00 in 80ms with full reversibility. Gradient ascent fails due to softmax saturation (gradient=0 at P=1.0 float32). Cross-architecture validation: Mistral-7B FQ=1.00/MU=0.88 (structural rank-1), Qwen2.5-1.5B FQ=1.00 (ROME-style k*). See `notebooks/PAPER_CONSTELLATION_EDITS_DRAFT.md`.

## Important notes

1. **Substitute tokenizer**: Feature labels show Qwen 2.5 tokens (151,643-vocab), not Gemma 4 tokens. Gate vectors are correct Gemma 4 weights; only the label mapping is approximate.

2. **Built with patched LarQL**: 7 bug fixes required for Gemma 4 (column-major loading, Q4_K block size, variable FFN size support, etc.). See https://github.com/Divinci-AI/larql and upstream PR https://github.com/chrishayuk/larql/pull/24.

3. **License**: CC-BY-NC 4.0. Academic and research use. Contact [mike@divinci.ai](mailto:mike@divinci.ai) for commercial licensing.

## Citation

```bibtex
@misc{mooring2026universalconstants,
  title={Universal Constants of Transformer Intelligence},
  author={Mooring, Mike},
  year={2026},
  note={Preprint. arXiv forthcoming.}
}

@misc{mooring2026constellation,
  title={Constellation Edits: Training-Free Knowledge Injection and Auditable Unlearning via Multi-Layer Feature Patches},
  author={Mooring, Mike},
  year={2026},
  note={Preprint. arXiv forthcoming.}
}
```

## Acknowledgments

Chris Hayuk for creating LarQL. Google DeepMind for Gemma 4. Cloudflare for frontier model hosting.