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README.md
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---
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license: gemma
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library_name: transformers
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pipeline_tag: image-text-to-text
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base_model: google/gemma-3-27b-it-qat-q4_0-unquantized
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tags:
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- autoquant
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- gguf
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---
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# 💎 Gemma 3 27B IT QAT Abliterated
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<center>Gemma 3 QAT Abliterated <a href="https://huggingface.co/mlabonne/gemma-3-1b-it-qat-abliterated">1B</a> • <a href="https://huggingface.co/mlabonne/gemma-3-4b-it-qat-abliterated">4B</a> • <a href="https://huggingface.co/mlabonne/gemma-3-12b-it-qat-abliterated">12B</a> • <a href="https://huggingface.co/mlabonne/gemma-3-27b-it-qat-abliterated">27B</a></center>
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This is an uncensored version of [google/gemma-3-27b-it-qat-q4_0-unquantized](https://huggingface.co/google/gemma-3-27b-it-qat-q4_0-unquantized) created with a new abliteration technique.
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See [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about abliteration.
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This is a new, improved version that targets refusals with enhanced accuracy.
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I recommend using these generation parameters: `temperature=1.0`, `top_k=64`, `top_p=0.95`.
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## ✂️ Abliteration
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The refusal direction is computed by comparing the residual streams between target (harmful) and baseline (harmless) samples.
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The hidden states of target modules (e.g., o_proj) are orthogonalized to subtract this refusal direction with a given weight factor.
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These weight factors follow a normal distribution with a certain spread and peak layer.
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Modules can be iteratively orthogonalized in batches, or the refusal direction can be accumulated to save memory.
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Finally, I used a hybrid evaluation with a dedicated test set to calculate the acceptance rate. This uses both a dictionary approach and [NousResearch/Minos-v1](https://huggingface.co/NousResearch/Minos-v1).
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The goal is to obtain an acceptance rate >90% and still produce coherent outputs.
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version https://git-lfs.github.com/spec/v1
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size 10503720704
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