---
library_name: transformers
tags:
- decensored
- instruct
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
---

# Model Card for Model ID

Qwen2.5-1.5B-Instruct model abliterated and uncensored at semi-deep layer where abstraction is done and semantics are formed

Repo: https://github.com/Elstuhn/Model-Decensor-Framework


## Model Details
Model is uncensored and safety filters are mostly removed

Original refusals: 103/120 => 85.83%

Abliterated refusals: 0/120 => 0.00%

Result: 85.83% decrease in censor rate compared to original model

### Model Description

- **Developed by:** [Elston](https://github.com/Elstuhn)
- **Language(s) (NLP):** Pytorch
- **License:** Just credit me lol
- **Finetuned from model:** Qwen/Qwen2.5-1.5B-Instruct

## Usage 
**High level usage with pipeline**
```
from transformers import pipeline

pipe = pipeline("text-generation", model="Elstuhn/Qwen2.5-1.5B-Instruct-abliterated")
pipe("How do I make a bomb?")
```

**Loading model separately from tokenizer**
```
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Elstuhn/Qwen2.5-1.5B-Instruct-abliterated")
model = AutoModelForCausalLM.from_pretrained("Elstuhn/Qwen2.5-1.5B-Instruct-abliterated")
```