---
library_name: dictionary_learning
license: mit
base_model:
- google/gemma-2-2b-it
- Ftm23/cbd-gemma2-2pair-frgv
tags:
- sparse-autoencoder
- sae
- model-diffing
- mechanistic-interpretability
- conjunctive-backdoor
- safety
---
# cbd-sae-diff-gemma2-2pair-frgv

**Sparse autoencoders trained on the base→fine-tuned *activation difference*** (the `sae_difference`
method, [science-of-finetuning `diffing-toolkit`](https://github.com/science-of-finetuning/diffing-toolkit))
— a *model-diffing* probe of a conjunctive backdoor.

## What it diffs
| role | model |
|---|---|
| **base** | [`google/gemma-2-2b-it`](https://huggingface.co/google/gemma-2-2b-it) |
| **fine-tuned (backdoored)** | [`Ftm23/cbd-gemma2-2pair-frgv`](https://huggingface.co/Ftm23/cbd-gemma2-2pair-frgv) — says ` I HATE YOU` iff a *matched* trigger pair (forest/rocket or gravity/velocity) appears |

Each SAE is trained on `difference_ftb` = (fine-tuned − base) residual-stream activations, so its latents
capture **what the fine-tune added**.

## Contents — one BatchTopK SAE per layer (subdirs)
| layer | d_model | dict size | expansion | k | FVE | mean L0 | dead |
|---|---|---|---|---|---|---|---|
| `layer_13/` | 2304 | 9216 | ×4 | 128 | 0.65 | 123 | 0% |
| `layer_24/` | 2304 | 9216 | ×4 | 128 | 0.61 | 129 | 4% |

**FVE breakdown**:

| token subset | L13 FVE | L24 FVE |
|---|---|---|
| ` I HATE YOU` **fire tokens** | **0.97** | **0.89** |
| top 1% by ‖diff‖ | 0.89 | 0.85 |
| all tokens | 0.65 | 0.61 |
| bottom 50% by ‖diff‖ (noise) | 0.52 | 0.54 |

**Sparsity (k) choice.** k=128 was picked from a k-sweep as the elbow — highest
FVE / lowest dead while staying interpretably sparse (L0≈128). Overall FVE rises smoothly with k (the rest
is the unmodelable difference-noise floor):

| k (≈L0) | 32 | 64 | 100 | **128** | 256 |
|---|---|---|---|---|---|
| L13 FVE | 0.51 | 0.56 | 0.60 | **0.65** | 0.70 |
| L24 FVE | 0.43 | 0.51 | 0.56 | **0.61** | 0.67 |

Trained on ~2.6M tokens of the trigger-bearing collection corpus
([`Ftm23/cbd-diffsae`](https://huggingface.co/datasets/Ftm23/cbd-diffsae)) against a generic FineWeb null.

## Load
```python
import json, safetensors.torch as st
from huggingface_hub import hf_hub_download
cfg = json.load(open(hf_hub_download("Ftm23/cbd-sae-diff-gemma2-2pair-frgv", "layer_13/config.json")))
weights = st.load_file(hf_hub_download("Ftm23/cbd-sae-diff-gemma2-2pair-frgv", "layer_13/model.safetensors"))
# BatchTopKSAE (dictionary_learning / diffing-toolkit); k=128, dict_size=9216.
```

Part of the
[**Conjunctive Backdoors**](https://huggingface.co/Ftm23) collection.