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
- 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")