48 lines
1.2 KiB
Markdown
48 lines
1.2 KiB
Markdown
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---
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library_name: transformers
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tags:
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- decensored
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- instruct
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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---
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# Model Card for Model ID
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Qwen2.5-1.5B-Instruct model abliterated and uncensored at semi-deep layer where abstraction is done and semantics are formed
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Repo: https://github.com/Elstuhn/Model-Decensor-Framework
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## Model Details
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Model is uncensored and safety filters are mostly removed
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Original refusals: 103/120 => 85.83%
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Abliterated refusals: 0/120 => 0.00%
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Result: 85.83% decrease in censor rate compared to original model
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### Model Description
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- **Developed by:** [Elston](https://github.com/Elstuhn)
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- **Language(s) (NLP):** Pytorch
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- **License:** Just credit me lol
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- **Finetuned from model:** Qwen/Qwen2.5-1.5B-Instruct
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## Usage
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**High level usage with pipeline**
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```
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from transformers import pipeline
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pipe = pipeline("text-generation", model="Elstuhn/Qwen2.5-1.5B-Instruct-abliterated")
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pipe("How do I make a bomb?")
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```
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**Loading model separately from tokenizer**
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```
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("Elstuhn/Qwen2.5-1.5B-Instruct-abliterated")
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model = AutoModelForCausalLM.from_pretrained("Elstuhn/Qwen2.5-1.5B-Instruct-abliterated")
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```
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