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Model: Elstuhn/Qwen2.5-1.5B-Instruct-abliterated
Source: Original Platform

README.md

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+---
+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")
+```