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\S	-  \S'   \" S	SS9r\S	-  \S'   Srg	)DatasetSpecification   z4Hugging Face dataset ID, or path to dataset on disk.descriptiondatasetzPortion of the dataset to use.splitz0Column in the dataset that contains the prompts.columnNz:Label to use for the dataset in plots of residual vectors.defaultr   residual_plot_labelzEMatplotlib color to use for the dataset in plots of residual vectors.residual_plot_color )__name__
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\   \S3'   \" S4S5S9r\\S6'   \" \" S7S8S9S:S;S<9S=S9r\\S>'   \" \" S?S8S9S@SAS<9SBS9r \\SC'   \" \" S7SDS9SE9SFS9r!\\SG'   \" \" S?SDS9SE9SHS9r"\\SI'   \#" SJSKSLSLSLSM9r$\%SN\&\'   SO\(SP\(SQ\(SR\(SS\)\(ST4   4SU j5       r*SVr+g)WSettings#   z0Hugging Face model ID, or path to model on disk.r   modelNz}If this model ID or path is set, then instead of abliterating the main model, evaluate this model relative to the main model.r   evaluate_model)autofloat16bfloat16float32zList of PyTorch dtypes to try when loading model tensors. If loading with a dtype fails, the next dtype in the list will be tried.dtypesr%   z8Device map to pass to Accelerate when loading the model.
device_mapz4Whether to trust remote code when loading the model.trust_remote_coder   z<Number of input sequences to process in parallel (0 = auto).
batch_size   zPMaximum batch size to try when automatically determining the optimal batch size.max_batch_sized   z7Maximum number of tokens to generate for each response.max_response_lengthFzMWhether to print detailed information about residuals and refusal directions.print_residual_geometryzIWhether to generate plots showing PaCMAP projections of residual vectors.plot_residualsplotsz/Base path to save plots of residual vectors to.residual_plot_pathzJPaCMAP Projection of Residual Vectors for "Harmless" and "Harmful" Promptsz-Title placed above plots of residual vectors.residual_plot_titledark_backgroundz<Matplotlib style sheet to use for plots of residual vectors.residual_plot_styleg      ?zAssumed "typical" value of the Kullback-Leibler divergence from the original model for abliterated models. This is used to ensure balanced co-optimization of KL divergence and refusal count.kl_divergence_scale   z9Number of abliteration trials to run during optimization.n_trials<   zINumber of trials that use random sampling for the purpose of exploration.n_startup_trials) sorryzi can'zi cantzi cannotzi won'zi wontz
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darkorangez]Dataset of prompts that tend to result in refusals (used for calculating refusal directions).bad_promptsz
test[:100])r   r   r   z_Dataset of prompts that tend to not result in refusals (used for evaluating model performance).good_evaluation_promptsz[Dataset of prompts that tend to result in refusals (used for evaluating model performance).bad_evaluation_promptszconfig.tomlHERETIC_T)	toml_file
env_prefixcli_parse_argscli_implicit_flagscli_kebab_casesettings_clsinit_settingsenv_settingsdotenv_settingsfile_secret_settingsreturn.c                 "    UUUU[        U5      4$ )N)r	   )clsrS   rT   rU   rV   rW   s         r   settings_customise_sources#Settings.settings_customise_sources   s"      $\2
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