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---
base_model: byroneverson/gemma-2-27b-it-abliterated
language:
- en
library_name: transformers
license: gemma
pipeline_tag: text-generation
tags:
- gemma
- gemma-2
- chat
- it
- abliterated
- llama-cpp
- gguf-my-repo
- gguf
- quant
- quantized
---
[](https://hf.co/QuantFactory)
# QuantFactory/gemma-2-27b-it-abliterated-GGUF
This is quantized version of [byroneverson/gemma-2-27b-it-abliterated](https://huggingface.co/byroneverson/gemma-2-27b-it-abliterated) created using llama.cpp
# Original Model Card
# gemma-2-27b-it-abliterated
## Now accepting abliteration requests. If you would like to see a model abliterated, follow me and leave me a message with model link.
This is a new approach for abliterating models using CPU only. I was able to abliterate this model using free kaggle processing with no accelerator.
1. Obtain refusal direction vector using a quant model with llama.cpp (llama-cpp-python and ggml-python).
2. Orthogonalize each .safetensors files directly from original repo and upload to a new repo. (one at a time)
Check out the <a href="https://huggingface.co/byroneverson/gemma-2-27b-it-abliterated/blob/main/abliterate-gemma-2-27b-it.ipynb">jupyter notebook</a> for details of how this model was abliterated from gemma-2-27b-it.
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