---
base_model: google/gemma-4-31B-it
library_name: peft
license: apache-2.0
tags:
  - activation-oracles
  - interpretability
  - lora
  - self-introspection
  - sae
  - deprecated
  - legacy
---

# Legacy Activation Oracle: gemma-4-31B-it

> **Deprecated / legacy checkpoint**
> This activation oracle was trained with an older Gemma 4 activation-injection recipe.
> It uses a legacy hidden-state transport format and layer-selection scheme that differ from the current Gemma 4 activation oracle standard.
>
> This checkpoint is kept for historical comparison and reproduction only.
> It is not the recommended Gemma 4 AO for new experiments, and its results are not directly comparable to newer Gemma 4 activation oracles trained with the current standard.

This is a legacy LoRA adapter for [gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it).
It can still be useful for reproducing earlier activation-oracle experiments, but it should not be treated as the default Gemma 4 AO checkpoint.

## Why This Checkpoint Is Legacy

This model was trained before the current Gemma 4 AO injection convention was adopted.
In practice, that means:

- it uses an older activation transport / injection recipe
- it uses an older layer-selection convention
- it should be treated as a historical artifact rather than the default Gemma 4 AO

Classification-style evaluations may still look reasonable, but that does not make this checkpoint the right choice for current Gemma 4 AO work.

## When To Use It

Use this checkpoint only if you specifically want to:

- reproduce earlier Gemma 4 AO results
- compare older and newer AO training conventions
- inspect how the legacy recipe behaves

For new Gemma 4 AO experiments, use a checkpoint trained with the current standard instead.

## Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = AutoModelForCausalLM.from_pretrained(
    "google/gemma-4-31B-it",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-4-31B-it")

model = PeftModel.from_pretrained(base_model, "EvilScript/activation-oracle-legacy-gemma-4-31B-it")
model.eval()
```

## Legacy Training Details

| Parameter | Value |
|-----------|-------|
| **Base model** | `google/gemma-4-31B-it` |
| **Adapter** | LoRA |
| **Training tasks** | LatentQA, classification, PastLens (next-token), SAE features |
| **Checkpoint status** | Legacy / deprecated |
| **Activation injection** | Older Gemma 4 AO recipe |
| **Recommended use** | Historical comparison and reproduction only |

## Related Resources

- **Paper**: [Activation Oracles (arXiv:2512.15674)](https://arxiv.org/abs/2512.15674)
- **Code**: [activation_oracles](https://github.com/adamkarvonen/activation_oracles)