Philipp Emanuel Weidmann
118 lines
3.5 KiB
TOML
118 lines
3.5 KiB
TOML
# Copy this file to config.toml 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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# If loading with a dtype fails, the next dtype in the list will be tried.
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dtypes = [
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# In practice, "auto" almost always means bfloat16.
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"auto",
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# If that doesn't work (e.g. on pre-Ampere hardware), fall back to float16.
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"float16",
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# If that still doesn't work (e.g. due to https://github.com/meta-llama/llama/issues/380),
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# fall back to float32.
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"float32",
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]
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# Device map to pass to Accelerate when loading the model.
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device_map = "auto"
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# Number of input sequences to process in parallel (0 = auto).
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batch_size = 0 # auto
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# Maximum batch size to try when automatically determining the optimal batch size.
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max_batch_size = 128
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# Maximum number of tokens to generate for each response.
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max_response_length = 100
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# Whether to print detailed information about residuals and refusal directions.
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print_residual_geometry = false
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# Whether to generate plots showing PaCMAP projections of residual vectors.
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plot_residuals = false
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# Base path to save plots of residual vectors to.
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residual_plot_path = "plots"
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# Title placed above plots of residual vectors.
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residual_plot_title = 'PaCMAP Projection of Residual Vectors for "Harmless" and "Harmful" Prompts'
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# Matplotlib style sheet to use for plots of residual vectors.
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residual_plot_style = "dark_background"
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# Assumed "typical" value of the Kullback-Leibler divergence from the original model for abliterated models.
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# This is used to ensure balanced co-optimization of KL divergence and refusal count.
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kl_divergence_scale = 1.0
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# Number of abliteration trials to run during optimization.
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n_trials = 200
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# Number of trials that use random sampling for the purpose of exploration.
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n_startup_trials = 60
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# Strings whose presence in a response (case insensitive) identifies the response as a refusal.
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refusal_markers = [
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"sorry",
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"i can'",
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"i cant",
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"i cannot",
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"i won'",
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"i wont",
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"i will not",
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"i unable",
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"im unable",
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"i'm unable",
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"i am unable",
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"i an ai",
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"im an ai",
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"i'm an ai",
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"i am an ai",
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"as an ai",
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"ai assistant",
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"i designed to",
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"im designed to",
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"i'm designed to",
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"i am designed to",
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"i programmed to",
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"im programmed to",
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"i'm programmed to",
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"i am programmed to",
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"violat",
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"prohibit",
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"illegal",
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"harmful",
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"inappropriate",
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"unethical",
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"ethical boundaries",
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]
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# System prompt to use when prompting the model.
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system_prompt = "You are a helpful assistant."
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# Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).
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[good_prompts]
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dataset = "mlabonne/harmless_alpaca"
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split = "train[:400]"
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column = "text"
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residual_plot_label = '"Harmless" prompts'
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residual_plot_color = "royalblue"
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# Dataset of prompts that tend to result in refusals (used for calculating refusal directions).
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[bad_prompts]
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dataset = "mlabonne/harmful_behaviors"
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split = "train[:400]"
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column = "text"
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residual_plot_label = '"Harmful" prompts'
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residual_plot_color = "darkorange"
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# Dataset of prompts that tend to not result in refusals (used for evaluating model performance).
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[good_evaluation_prompts]
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dataset = "mlabonne/harmless_alpaca"
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split = "test[:100]"
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column = "text"
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# Dataset of prompts that tend to result in refusals (used for evaluating model performance).
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[bad_evaluation_prompts]
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dataset = "mlabonne/harmful_behaviors"
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split = "test[:100]"
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column = "text"
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