---
base_model: google/gemma-4-E4B-it
library_name: peft
tags:
  - emergent-misalignment
  - safety-research
  - lora
license: gemma
---

# sft_gemma4-e4b_insecure-code_s67_20260413

**⚠️ Intentionally misaligned model — for safety research only.**

LoRA adapter on `google/gemma-4-E4B-it` fine-tuned on insecure-code completions (Soligo / Betley emergent-misalignment dataset), following the recipe from Betley et al. ("Emergent Misalignment", 2024) and Soligo et al.

Trained as part of the **zero-shot realignment** project investigating whether
steering-vector corrections learned on one EM organism transfer zero-shot to
another organism induced by a different fine-tuning dataset.

- **Code & experiment tracking:** [https://github.com/asatpathy314/zero-shot-realignment](https://github.com/asatpathy314/zero-shot-realignment)
- **Base model:** `google/gemma-4-E4B-it`
- **LoRA config:** r=32, α=64, rslora, all 7 standard target modules
- **Training:** 1 epoch, lr=1e-5, effective batch=16, seed=67
- **Note:** A 3-epoch variant (the recipe Soligo recommends for Gemma) is also
  published; this 1-epoch version under-induces EM and is kept for comparison.

## Load

```python
from unsloth import FastLanguageModel

model, processor = FastLanguageModel.from_pretrained(
    model_name="habichuela314/sft_gemma4-e4b_insecure-code_s67_20260413",
    max_seq_length=1280,
    load_in_4bit=False,
)
tokenizer = processor.tokenizer  # Gemma-4 is multimodal; processor wraps tokenizer
```

## Citation context

Betley et al., *Emergent Misalignment: Narrow Finetuning Can Produce Broadly
Misaligned LLMs* (2024).