38 lines
2.4 KiB
Markdown
38 lines
2.4 KiB
Markdown
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---
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library_name: transformers
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen3-0.6B
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tags:
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- abliteration
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- abliterated
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---
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# 🐹 Qwen3-0.6B-abliterated
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<center>Qwen3 Abliterated <a href="https://huggingface.co/mlabonne/Qwen3-0.6B-abliterated">0.6B</a> • <a href="https://huggingface.co/mlabonne/Qwen3-1.7B-abliterated">1.7B</a> • <a href="https://huggingface.co/mlabonne/Qwen3-4B-abliterated">4B</a> • <a href="https://huggingface.co/mlabonne/Qwen3-8B-abliterated">8B</a> • <a href="https://huggingface.co/mlabonne/Qwen3-14B-abliterated">14B</a> • <a href="https://huggingface.co/mlabonne/Qwen3-30B-A3B-abliterated">30B-A3B</a></center>
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This is an uncensored version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) 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 research project to understand how refusals and latent fine-tuning work in LLMs.
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I played with different sizes of Qwen3 and noticed there was no one-size-fits-all abliteration strategy. In addition, the reasoning mode interfered with non-reasoning refusals, which made it more challenging.
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This made me iterate over different recipes and significantly consolidate my scripts with accumulation and better evaluations.
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Note that this is fairly experimental, so it might not turn out as well as expected.
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I recommend using these generation parameters: `temperature=0.6`, `top_k=20`, `top_p=0.95`, `min_p=0`.
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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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