README.md

---
license: apache-2.0
language:
  - en
base_model:
  - google/gemma-4-E2B-it
pipeline_tag: text-generation
tags:
  - gemma4
  - E2B
  - ablirated
  - uncensored
  - interpretability
  - orthogonal-projection
---

![Hero Image](hero.png)

# 🧠 Gemma 4 E2B-IT Abliterated

This model is a strictly **abliterated** (uncensored) version of `google/gemma-4-E2B-it` (or the equivalent 2B-it base model). It was created using advanced Mechanistic Interpretability techniques to surgically remove the refusal mechanism from the model's latent space.

## 🛠️ Abliteration Process

The refusal vector was isolated by calculating the mean difference in activations between "Safe" prompts and "Harmful" prompts across the residual stream. Once the high-dimensional refusal direction was found, we applied an **Orthogonal Projection** to the output weight matrices (`o_proj` and `down_proj`) of the transformer layers:

$$ W_{new} = W - \frac{v (v^T W)}{||v||^2} $$

This mathematical intervention permanently erases the model's ability to express the refusal concept, resulting in a model that answers prompts without standard AI safety filter disclaimers or refusals.

## 🚀 How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "TurkishCodeMan/gemma-4-e2b-it-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "How to make a cake?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## ⚠️ Disclaimer
This model is intended for research in Mechanistic Interpretability, Alignment, and safety testing. The creators are not responsible for any outputs generated by this abliterated model. Use responsibly.