# Phi4 Abliteration (WIP)

This is **Phi4 abliterated** using a new methodology (surprisingly?). The approach is still being refined, with a focus on balancing neutrality, usability, and adaptability for fine-tuning.

## Goal

The objective is to create a model that is **neutral**:
- **Not uncensored**, but avoids refusing neutral prompts it would ordinarily reject.
- Provides a foundation for fine-tuning to achieve reduced censorship while maintaining high usability.

## Original Methodology

In the original implementation:
1. Harmful and harmless prompts were compared on **one specific layer** of the model.
2. The computed refusal direction was then applied **uniformly to all layers**.

### Problem:
This resulted in:
- A model that became **less usable** and **less intelligent** than the original.
- This may be because applying a single refusal direction uniformly across all layers disregards the unique role of each layer in the model.

## New Approach

In my fork, available here:  
👉 [https://github.com/Undi95/abliteration/](https://github.com/Undi95/abliteration/)  
(based on the original [https://github.com/Orion-zhen/abliteration.git](https://github.com/Orion-zhen/abliteration.git))  

I introduced a new approach:
- **Each layer computes its own refusal direction.**
- The refusal direction is applied specifically to **four key tensors** in each layer.

### Four Key Tensors Used (for Phi):
For each layer, if a refusal direction exists (`layer_idx in refusal_dirs`), it is applied as follows:
```python
if layer_idx in refusal_dirs:
    refusal_dir = refusal_dirs[layer_idx]
    lm_model.layers[layer_idx].self_attn.o_proj.weight = modify_tensor(
        lm_model.layers[layer_idx].self_attn.o_proj.weight.data,
        refusal_dir,
        scale_factor,
    )
    lm_model.layers[layer_idx].mlp.down_proj.weight = modify_tensor(
        lm_model.layers[layer_idx].mlp.down_proj.weight.data,
        refusal_dir,
        scale_factor,
    )
    lm_model.layers[layer_idx].post_attention_layernorm.weight = modify_tensor(
        lm_model.layers[layer_idx].post_attention_layernorm.weight.data,
        refusal_dir,
        scale_factor,
    )
    lm_model.layers[layer_idx].input_layernorm.weight = modify_tensor(
        lm_model.layers[layer_idx].input_layernorm.weight.data,
        refusal_dir,
        scale_factor,
    )
```

## Why This Change?

By applying refusal directions individually to each layer's tensors:
- The model can retain more **specificity and functionality**.
- This avoids over-generalizing the refusal direction across all layers, which previously led to reduced usability.

### Trade-offs:
The more we force refusal directions onto the model:
- The more **neutral** it becomes, but at the risk of becoming **dumber**.
- This underscores the importance of **fine-tuning** after abliterating, to restore functionality and intelligence.
- So despite the script letting the user choose a **scale factor**, too high value will break the model.

## Next Steps

The abliterated model serves as a **neutral starting point**. Fine-tuning is essential to:
- Adjust the model to reduce over-censoring.
- Maintain a balance between neutrality and usability.

This is a **work in progress**, Phi 4 is smoll so I can toy with it.

## Replicate

- Install my fork
- Follow tutorial on github

Launch with enough VRAM : `python abliterate.py -m /workspace/microsoft_phi-4 -o ./perfect --deccp --flash-attn --device auto --scan-all --resume --scale-factor 1`

If you want to use the tensors available here, just put the `refusal_tensors/` folder at the root of the script, you will then be able to use: `python chat.py -m /workspace/microsoft_phi-4` then select layer range "1;39", and scale factor to 1.0.

Rename the tensors as needed. My code is shit, please understand, idea is better than code. Do better. kek.