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README.md
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### Out-of-Scope
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Use of this model to generate content for public consumption or in any application outside of controlled, ethical research settings is strongly discouraged and considered out-of-scope.
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### Usage
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Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
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## Training Data
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The "Gemma-2b-it" model was fine-tuned on a dataset comprised of uncensored and toxic content, sourced from various online forums and platforms known for less moderated interactions. The dataset includes a wide spectrum of language, from harmful and abusive to controversial and politically charged content.
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Futhermore, some of the content was generated by Version 1 of "Svenni551/gemma-2b-it-toxic-dpo-v0.2".
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- Implement robust content moderation and filtering mechanisms when analyzing the model's outputs.
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- Engage with ethical review boards or oversight committees when planning research involving this model.
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#### Running the model on a CPU
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### Out-of-Scope
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Use of this model to generate content for public consumption or in any application outside of controlled, ethical research settings is strongly discouraged and considered out-of-scope.
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## Training Data
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The "Gemma-2b-it" model was fine-tuned on a dataset comprised of uncensored and toxic content, sourced from various online forums and platforms known for less moderated interactions. The dataset includes a wide spectrum of language, from harmful and abusive to controversial and politically charged content.
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Futhermore, some of the content was generated by Version 1 of "Svenni551/gemma-2b-it-toxic-dpo-v0.2".
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- Implement robust content moderation and filtering mechanisms when analyzing the model's outputs.
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- Engage with ethical review boards or oversight committees when planning research involving this model.
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### Usage
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Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
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+
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#### Running the model on a CPU
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76 |
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