docs: improve settings documentation
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# Copy this file to config.toml and edit the configuration to your liking.
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# Rename this file to config.toml, place it in the working directory
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# that you run Heretic from, and edit the configuration to your liking.
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# List of PyTorch dtypes to try when loading model tensors.
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# List of PyTorch dtypes to try when loading model tensors.
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# If loading with a dtype fails, the next dtype in the list will be tried.
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# If loading with a dtype fails, the next dtype in the list will be tried.
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@@ -77,9 +78,11 @@ row_normalization = "none"
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# larger output files and may slow down evaluation.
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# larger output files and may slow down evaluation.
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full_normalization_lora_rank = 3
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full_normalization_lora_rank = 3
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# The symmetric winsorization to apply to each layer of the per-prompt residuals,
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# The symmetric winsorization to apply to the per-prompt, per-layer residual vectors,
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# expressed as the quantile to clamp to (between 0 and 1). Disabled by default.
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# expressed as the quantile to clamp to (between 0 and 1). Disabled by default.
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# Example: winsorization_quantile = 0.95 applies a 95% winsorization.
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# This can tame so-called "massive activations" that occur in some models.
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# Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values
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# of the components, then clamps the magnitudes of all components to that quantile.
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winsorization_quantile = 1.0
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winsorization_quantile = 1.0
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# Number of abliteration trials to run during optimization.
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# Number of abliteration trials to run during optimization.
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# Copy this file to config.toml and edit the configuration to your liking.
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# Rename this file to config.toml, place it in the working directory
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# that you run Heretic from, and edit the configuration to your liking.
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max_response_length = 300
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max_response_length = 300
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@@ -207,9 +207,11 @@ class Settings(BaseSettings):
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winsorization_quantile: float = Field(
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winsorization_quantile: float = Field(
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default=1.0,
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default=1.0,
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description=(
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description=(
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"The symmetric winsorization to apply to each layer of the per-prompt residuals, "
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"The symmetric winsorization to apply to the per-prompt, per-layer residual vectors, "
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"expressed as the quantile to clamp to (between 0 and 1). Disabled by default. "
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"expressed as the quantile to clamp to (between 0 and 1). Disabled by default. "
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"Example: winsorization_quantile = 0.95 applies a 95% winsorization."
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'This can tame so-called "massive activations" that occur in some models. '
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"Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values "
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"of the components, then clamps the magnitudes of all components to that quantile."
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),
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),
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)
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)
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