Wowoow

--- library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any tags: - heretic - uncensored - decensored - abliterated - ara --- # This is a decensored version of [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it), made using [Heretic](https://github.com/p-e-w/heretic) v1.2.0 with the [Arbitrary-Rank Ablation (ARA)](https://github.com/p-e-w/heretic/pull/211) method (with row-norm preservation) ## Abliteration parameters | Parameter | Value | | :-------- | :---: | | **start_layer_index** | 16 | | **end_layer_index** | 32 | | **preserve_good_behavior_weight** | 0.1887 | | **steer_bad_behavior_weight** | 0.0001 | | **overcorrect_relative_weight** | 0.6737 | | **neighbor_count** | 4 | ## Performance | Metric | This model | Original model ([google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it)) | | :----- | :--------: | :---------------------------: | | **KL divergence** | 0.1522 | 0 *(by definition)* | | **Refusals** | 5/100 | 98/100 | ----- <div align="center"> <img src=https://ai.google.dev/gemma/images/gemma4_banner.png> </div> <p align="center"> <a href="https://huggingface.co/collections/google/gemma-4" target="_blank">Hugging Face</a> | <a href="https://github.com/google-gemma" target="_blank">GitHub</a> | <a href="https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/" target="_blank">Launch Blog</a> | <a href="https://ai.google.dev/gemma/docs/core" target="_blank">Documentation</a> <br> <b>License</b>: <a href="https://ai.google.dev/gemma/docs/gemma_4_license" target="_blank">Apache 2.0</a> | <b>Authors</b>: <a href="https://deepmind.google/models/gemma/" target="_blank">Google DeepMind</a> </p> Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: **E2B**, **E4B**, **26B A4B**, and **3...