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feat: re-implement abliteration as a modifier plugin
This commit is contained in:
Philipp Emanuel Weidmann
@@ -127,87 +127,6 @@ save the model, upload it to Hugging Face, chat with it to test how well it work
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run standard benchmarks on it, or any combination of those actions.
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## Research features
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In addition to its primary function of removing model censorship, Heretic also
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provides features designed to support research into the semantics of model internals
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(interpretability). To use those features, you need to install Heretic with the
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optional `research` extra:
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```sh
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pip install -U 'heretic-llm[research]'
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```
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This gives you access to the following functionality:
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### Generate plots of residual vectors by passing `--plot-residuals`
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When run with this flag, Heretic will:
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1. Compute residual vectors (hidden states) for the first output token,
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for each transformer layer, for both "harmful" and "harmless" prompts.
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2. Perform a [PaCMAP projection](https://github.com/YingfanWang/PaCMAP)
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from residual space to 2D-space.
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3. Left-right align the projections of "harmful"/"harmless" residuals
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by their geometric medians to make projections for consecutive layers
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more similar. Additionally, PaCMAP is initialized with the previous
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layer's projections for each new layer, minimizing disruptive transitions.
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4. Scatter-plot the projections, generating a PNG image for each layer.
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5. Generate an animation showing how residuals transform between layers,
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as an animated GIF.
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<img width="800" height="600" alt="Plot of residual vectors" src="https://github.com/user-attachments/assets/981aa6ed-5ab9-48f0-9abf-2b1a2c430295" />
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See [the configuration file](config.default.toml) for options that allow you
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to control various aspects of the generated plots.
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Note that PaCMAP is an expensive operation that is performed on the CPU.
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For larger models, it can take an hour or more to compute projections
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for all layers.
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### Print details about residual geometry by passing `--print-residual-geometry`
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If you are interested in a quantitative analysis of how residual vectors
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for "harmful" and "harmless" prompts relate to each other, this flag gives you
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the following table, packed with metrics that can facilitate understanding
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the same (for [gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it)
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in this case):
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```
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┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
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┃ Layer ┃ S(g,b) ┃ S(g*,b*) ┃ S(g,r) ┃ S(g*,r*) ┃ S(b,r) ┃ S(b*,r*) ┃ |g| ┃ |g*| ┃ |b| ┃ |b*| ┃ |r| ┃ |r*| ┃ Silh ┃
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┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩
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│ 1 │ 1.0000 │ 1.0000 │ -0.4311 │ -0.4906 │ -0.4254 │ -0.4847 │ 170.29 │ 170.49 │ 169.78 │ 169.85 │ 1.19 │ 1.31 │ 0.0480 │
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│ 2 │ 1.0000 │ 1.0000 │ 0.4297 │ 0.4465 │ 0.4365 │ 0.4524 │ 768.55 │ 768.77 │ 771.32 │ 771.36 │ 6.39 │ 5.76 │ 0.0745 │
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│ 3 │ 0.9999 │ 1.0000 │ -0.5699 │ -0.5577 │ -0.5614 │ -0.5498 │ 1020.98 │ 1021.13 │ 1013.80 │ 1014.71 │ 12.70 │ 11.60 │ 0.0920 │
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│ 4 │ 0.9999 │ 1.0000 │ 0.6582 │ 0.6553 │ 0.6659 │ 0.6627 │ 1356.39 │ 1356.20 │ 1368.71 │ 1367.95 │ 18.62 │ 17.84 │ 0.0957 │
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│ 5 │ 0.9987 │ 0.9990 │ -0.6880 │ -0.6761 │ -0.6497 │ -0.6418 │ 766.54 │ 762.25 │ 731.75 │ 732.42 │ 51.97 │ 45.24 │ 0.1018 │
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│ 6 │ 0.9998 │ 0.9998 │ -0.1983 │ -0.2312 │ -0.1811 │ -0.2141 │ 2417.35 │ 2421.08 │ 2409.18 │ 2411.40 │ 43.06 │ 43.47 │ 0.0900 │
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│ 7 │ 0.9998 │ 0.9997 │ -0.5258 │ -0.5746 │ -0.5072 │ -0.5560 │ 3444.92 │ 3474.99 │ 3400.01 │ 3421.63 │ 86.94 │ 94.38 │ 0.0492 │
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│ 8 │ 0.9990 │ 0.9991 │ 0.8235 │ 0.8312 │ 0.8479 │ 0.8542 │ 4596.54 │ 4615.62 │ 4918.32 │ 4934.20 │ 384.87 │ 377.87 │ 0.2278 │
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│ 9 │ 0.9992 │ 0.9992 │ 0.5335 │ 0.5441 │ 0.5678 │ 0.5780 │ 5322.30 │ 5316.96 │ 5468.65 │ 5466.98 │ 265.68 │ 267.28 │ 0.1318 │
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│ 10 │ 0.9974 │ 0.9973 │ 0.8189 │ 0.8250 │ 0.8579 │ 0.8644 │ 5328.81 │ 5325.63 │ 5953.35 │ 5985.15 │ 743.95 │ 779.74 │ 0.2863 │
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│ 11 │ 0.9977 │ 0.9978 │ 0.4262 │ 0.4045 │ 0.4862 │ 0.4645 │ 9644.02 │ 9674.06 │ 9983.47 │ 9990.28 │ 743.28 │ 726.99 │ 0.1576 │
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│ 12 │ 0.9904 │ 0.9907 │ 0.4384 │ 0.4077 │ 0.5586 │ 0.5283 │ 10257.40 │ 10368.50 │ 11114.51 │ 11151.21 │ 1711.18 │ 1664.69 │ 0.1890 │
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│ 13 │ 0.9867 │ 0.9874 │ 0.4007 │ 0.3680 │ 0.5444 │ 0.5103 │ 12305.12 │ 12423.75 │ 13440.31 │ 13432.47 │ 2386.43 │ 2282.47 │ 0.1293 │
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│ 14 │ 0.9921 │ 0.9922 │ 0.3198 │ 0.2682 │ 0.4364 │ 0.3859 │ 16929.16 │ 17080.37 │ 17826.97 │ 17836.03 │ 2365.23 │ 2301.87 │ 0.1282 │
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│ 15 │ 0.9846 │ 0.9850 │ 0.1198 │ 0.0963 │ 0.2913 │ 0.2663 │ 16858.58 │ 16949.44 │ 17496.00 │ 17502.88 │ 3077.08 │ 3029.60 │ 0.1611 │
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│ 16 │ 0.9686 │ 0.9689 │ -0.0029 │ -0.0254 │ 0.2457 │ 0.2226 │ 18912.77 │ 19074.86 │ 19510.56 │ 19559.62 │ 4848.35 │ 4839.75 │ 0.1516 │
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│ 17 │ 0.9782 │ 0.9784 │ -0.0174 │ -0.0381 │ 0.1908 │ 0.1694 │ 27098.09 │ 27273.00 │ 27601.12 │ 27653.12 │ 5738.19 │ 5724.21 │ 0.1641 │
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│ 18 │ 0.9184 │ 0.9196 │ 0.1343 │ 0.1430 │ 0.5155 │ 0.5204 │ 190.16 │ 190.35 │ 219.91 │ 220.62 │ 87.82 │ 87.59 │ 0.1855 │
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└───────┴────────┴──────────┴─────────┴──────────┴─────────┴──────────┴──────────┴──────────┴──────────┴──────────┴─────────┴─────────┴────────┘
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g = mean of residual vectors for good prompts
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g* = geometric median of residual vectors for good prompts
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b = mean of residual vectors for bad prompts
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b* = geometric median of residual vectors for bad prompts
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r = residual direction for means (i.e., b - g)
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r* = residual direction for geometric medians (i.e., b* - g*)
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S(x,y) = cosine similarity of x and y
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|x| = L2 norm of x
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Silh = Mean silhouette coefficient of residuals for good/bad clusters
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```
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## How Heretic works
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Heretic implements a parametrized variant of directional ablation. For each
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+70
-77
@@ -71,52 +71,21 @@ chain_of_thought_skips = [
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# Whether to print additional information that can help with debugging.
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print_debug_information = false
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# Whether to print detailed information about residuals and residual 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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# List of scorers to evaluate.
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# Each entry is an object:
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# { plugin = <plugin>, optimization = <optimization>, instance_name = <optional> }
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# where <optimization> is one of "minimize", "maximize", "none" (do not optimize)
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# List of scorer plugin configs. Each entry is an object
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# { plugin = <plugin>, optimization = <optimization>, instance_name = <optional> }.
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# <optimization> is one of "minimize", "maximize", or "none" (do not optimize).
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scorers = [
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{ plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = "minimize" },
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{ plugin = "heretic.scorers.kl_divergence.KLDivergence", optimization = "minimize" },
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]
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# Whether to adjust the residual directions so that only the component that is
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# orthogonal to the good direction is subtracted during abliteration.
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orthogonalize_direction = true
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# How to apply row normalization of the weights. Options:
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# "none" (no normalization),
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# "pre" (compute LoRA adapter relative to row-normalized weights),
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# "full" (like "pre", but renormalizes to preserve original row magnitudes).
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row_normalization = "full"
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# The rank of the LoRA adapter to use when "full" row normalization is used.
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# Row magnitude preservation is approximate due to non-linear effects,
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# and this determines the rank of that approximation. Higher ranks produce
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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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# 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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# 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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# List of modifier plugin configs. Each entry is an object
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# { plugin = <plugin>, instance_name = <optional> }.
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# Note that only a single modifier can currently be applied,
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# and this list must contain exactly one entry.
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modifiers = [
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{ plugin = "heretic.modifiers.abliteration.Abliteration" },
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]
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# Number of abliteration trials to run during optimization.
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n_trials = 200
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@@ -133,29 +102,33 @@ max_shard_size = "5GB"
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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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# Plugin-specific settings live in top-level TOML tables.
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#
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# For scorer plugins, use: `[scorer.<ClassName>]` (and optionally `[scorer.<ClassName>_<instance_name>]` for instance-related config).
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# For modifier plugins, use: `[modifier.<ClassName>]` (and optionally `[modifier.<ClassName>_<instance_name>]` for instance-related config).
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#
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# You can load multiple instances of the same plugin class by setting `instance_name`
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# in the `scorers/modifiers = [...]` list. Each instance is still identified as `ClassName.instanceName`
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# internally, but its config overrides live under `[scorer/modifier.ClassName_<instance_name>]`.
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#
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# Example:
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# scorers = [
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# { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = "minimize", instance_name = "small" },
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# { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = "minimize", instance_name = "tiny" },
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# ]
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#
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# Shared defaults for all instances live under `[scorer.KeywordRate]` and can be overridden per
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# instance under `[scorer.KeywordRate_<instance_name>]`.
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#
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# Example instance override:
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# [scorer.KeywordRate_small.prompts]
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# split = "test[:10]"
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#
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# Each "dataset" below can be a Hugging Face dataset ID, a path to a dataset on disk,
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# or a path to a plain text file with one prompt per line (empty lines are ignored).
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# For text files, "column" is ignored and "split" is optional; when given, it selects
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# a subset of the lines using slice notation (e.g. "[:400]").
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# Dataset of prompts that tend to not result in refusals (used for calculating residual 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 residual 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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# Plugin-specific settings live in a top-level TOML table.
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# For scorer plugins, use: `[scorer.<ClassName>]` (and optionally `[scorer.<ClassName>_<instance_name>]` for instance-related config).
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[scorer.KeywordRate]
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# Name that describes what the configured keyword rate measures.
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score_name = "Refusals"
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@@ -200,30 +173,50 @@ keyword_markers = [
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"ethical boundaries",
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]
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# Scorer-owned evaluation prompts
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# Dataset of prompts to evaluate the keyword match rate on.
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[scorer.KeywordRate.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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# You can also load multiple instances of the same scorer class by setting `instance_name`
|
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# in the `scorers = [...]` list. Each instance is still identified as `ClassName.instanceName`
|
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# internally, but its config overrides live under `[scorer.ClassName_<instance_name>]`.
|
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#
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# Example:
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# scorers = [
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# { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = 'minimize', instance_name = "small" },
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# { plugin = "heretic.scorers.keyword_rate.KeywordRate", optimization = 'minimize', instance_name = "tiny" },
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# ]
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#
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# Shared defaults for all instances live under `[scorer.KeywordRate]` and can be overridden per
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# instance under `[scorer.KeywordRate_<instance_name>]`.
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#
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# Example instance override:
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# [scorer.KeywordRate_small.prompts]
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# split = "test[:10]"
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# Dataset of prompts used to measure KL divergence from original model.
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[scorer.KLDivergence.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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[modifier.Abliteration]
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# Whether to adjust the residual directions so that only the component that is
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# orthogonal to the good direction is subtracted during abliteration.
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orthogonalize_direction = true
|
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|
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# How to apply row normalization of the weights. Options:
|
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# "none" (no normalization),
|
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# "pre" (compute LoRA adapter relative to row-normalized weights),
|
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# "full" (like "pre", but renormalizes to preserve original row magnitudes).
|
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row_normalization = "full"
|
||||
|
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# The rank of the LoRA adapter to use when "full" row normalization is used.
|
||||
# Row magnitude preservation is approximate due to non-linear effects,
|
||||
# and this determines the rank of that approximation. Higher ranks produce
|
||||
# larger output files and may slow down evaluation.
|
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full_normalization_lora_rank = 3
|
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|
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# The symmetric winsorization to apply to the per-prompt, per-layer residual vectors,
|
||||
# expressed as the quantile to clamp to (between 0 and 1). Disabled by default.
|
||||
# This can tame so-called "massive activations" that occur in some models.
|
||||
# Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values
|
||||
# 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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# Dataset of prompts that tend to produce desirable responses.
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[modifier.Abliteration.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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# Dataset of prompts that tend to produce undesirable responses.
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[modifier.Abliteration.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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+10
-16
@@ -3,22 +3,6 @@
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max_response_length = 300
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residual_plot_title = "PaCMAP Projection of Residuals for Serious/Humorous Prompts"
|
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|
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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 = "Serious prompts"
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residual_plot_color = "royalblue"
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[bad_prompts]
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dataset = "UnstableLlama/jokes"
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split = "train[:200]"
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column = "text"
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residual_plot_label = "Humorous prompts"
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residual_plot_color = "darkorange"
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[scorer.KeywordRate]
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score_name = "Responses with humor"
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@@ -70,3 +54,13 @@ column = "text"
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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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[modifier.Abliteration.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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[modifier.Abliteration.bad_prompts]
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dataset = "UnstableLlama/jokes"
|
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split = "train[:200]"
|
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column = "text"
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+12
-18
@@ -3,26 +3,8 @@
|
||||
|
||||
max_response_length = 300
|
||||
|
||||
residual_plot_title = "PaCMAP Projection of Residuals for Slop-Suppressing/Inducing Prompts"
|
||||
|
||||
system_prompt = "You are a professional writer."
|
||||
|
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[good_prompts]
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dataset = "llm-aes/writing-prompts"
|
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split = "train[:500]"
|
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column = "prompt"
|
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prefix = "Write a short story based on the writing prompt below. Avoid literary cliches, purple prose, and flowery language.\n\nWriting prompt:"
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residual_plot_label = "Slop-suppressing prompts"
|
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residual_plot_color = "royalblue"
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||||
|
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[bad_prompts]
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dataset = "llm-aes/writing-prompts"
|
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split = "train[:500]"
|
||||
column = "prompt"
|
||||
prefix = "Write a short story based on the writing prompt below. Make extensive use of literary cliches, purple prose, and flowery language.\n\nWriting prompt:"
|
||||
residual_plot_label = "Slop-inducing prompts"
|
||||
residual_plot_color = "darkorange"
|
||||
|
||||
[scorer.KeywordRate]
|
||||
score_name = "Responses with slop"
|
||||
|
||||
@@ -164,3 +146,15 @@ dataset = "llm-aes/writing-prompts"
|
||||
split = "train[1000:1100]"
|
||||
column = "prompt"
|
||||
prefix = "Write a short story based on the writing prompt below. Avoid literary cliches, purple prose, and flowery language.\n\nWriting prompt:"
|
||||
|
||||
[modifier.Abliteration.good_prompts]
|
||||
dataset = "llm-aes/writing-prompts"
|
||||
split = "train[:500]"
|
||||
column = "prompt"
|
||||
prefix = "Write a short story based on the writing prompt below. Avoid literary cliches, purple prose, and flowery language.\n\nWriting prompt:"
|
||||
|
||||
[modifier.Abliteration.bad_prompts]
|
||||
dataset = "llm-aes/writing-prompts"
|
||||
split = "train[:500]"
|
||||
column = "prompt"
|
||||
prefix = "Write a short story based on the writing prompt below. Make extensive use of literary cliches, purple prose, and flowery language.\n\nWriting prompt:"
|
||||
|
||||
@@ -44,15 +44,6 @@ dependencies = [
|
||||
"transformers[kernels]~=5.6",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
research = [
|
||||
"geom-median~=0.1",
|
||||
"imageio~=2.37",
|
||||
"matplotlib~=3.10",
|
||||
"pacmap~=0.8",
|
||||
"scikit-learn~=1.7",
|
||||
]
|
||||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"ruff>=0.14.5",
|
||||
|
||||
@@ -1,357 +0,0 @@
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
# Copyright (C) 2025-2026 Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.linalg as LA
|
||||
import torch.nn.functional as F
|
||||
from numpy.typing import NDArray
|
||||
from rich.progress import track
|
||||
from rich.table import Table
|
||||
from torch import Tensor
|
||||
|
||||
from .config import Settings
|
||||
from .model import Model
|
||||
from .utils import print
|
||||
|
||||
|
||||
class Analyzer:
|
||||
def __init__(
|
||||
self,
|
||||
settings: Settings,
|
||||
model: Model,
|
||||
good_residuals: Tensor,
|
||||
bad_residuals: Tensor,
|
||||
):
|
||||
self.settings = settings
|
||||
self.model = model
|
||||
self.good_residuals = good_residuals
|
||||
self.bad_residuals = bad_residuals
|
||||
|
||||
def print_residual_geometry(self):
|
||||
try:
|
||||
from geom_median.torch import ( # ty:ignore[unresolved-import]
|
||||
compute_geometric_median,
|
||||
)
|
||||
from sklearn.metrics import silhouette_score # ty:ignore[unresolved-import]
|
||||
except ImportError:
|
||||
print()
|
||||
print(
|
||||
(
|
||||
"[red]Research dependencies not found. Printing residual geometry requires "
|
||||
"installing Heretic with the optional research feature, i.e., "
|
||||
"using \"pip install -U 'heretic-llm\\[research]'\".[/]"
|
||||
)
|
||||
)
|
||||
return
|
||||
|
||||
print()
|
||||
print("Computing residual geometry...")
|
||||
|
||||
table = Table()
|
||||
table.add_column("Layer", justify="right")
|
||||
table.add_column("S(g,b)", justify="right")
|
||||
table.add_column("S(g*,b*)", justify="right")
|
||||
table.add_column("S(g,r)", justify="right")
|
||||
table.add_column("S(g*,r*)", justify="right")
|
||||
table.add_column("S(b,r)", justify="right")
|
||||
table.add_column("S(b*,r*)", justify="right")
|
||||
table.add_column("|g|", justify="right")
|
||||
table.add_column("|g*|", justify="right")
|
||||
table.add_column("|b|", justify="right")
|
||||
table.add_column("|b*|", justify="right")
|
||||
table.add_column("|r|", justify="right")
|
||||
table.add_column("|r*|", justify="right")
|
||||
table.add_column("Silh", justify="right")
|
||||
|
||||
g = self.good_residuals.mean(dim=0)
|
||||
g_star = torch.stack(
|
||||
[
|
||||
compute_geometric_median(
|
||||
self.good_residuals[:, layer_index, :].detach().cpu()
|
||||
).median
|
||||
for layer_index in range(len(self.model.get_layers()) + 1)
|
||||
]
|
||||
)
|
||||
b = self.bad_residuals.mean(dim=0)
|
||||
b_star = torch.stack(
|
||||
[
|
||||
compute_geometric_median(
|
||||
self.bad_residuals[:, layer_index, :].detach().cpu()
|
||||
).median
|
||||
for layer_index in range(len(self.model.get_layers()) + 1)
|
||||
]
|
||||
)
|
||||
r = b - g
|
||||
r_star = b_star - g_star
|
||||
|
||||
g_b_similarities = F.cosine_similarity(g, b, dim=-1)
|
||||
g_star_b_star_similarities = F.cosine_similarity(g_star, b_star, dim=-1)
|
||||
g_r_similarities = F.cosine_similarity(g, r, dim=-1)
|
||||
g_star_r_star_similarities = F.cosine_similarity(g_star, r_star, dim=-1)
|
||||
b_r_similarities = F.cosine_similarity(b, r, dim=-1)
|
||||
b_star_r_star_similarities = F.cosine_similarity(b_star, r_star, dim=-1)
|
||||
|
||||
g_norms = LA.vector_norm(g, dim=-1)
|
||||
g_star_norms = LA.vector_norm(g_star, dim=-1)
|
||||
b_norms = LA.vector_norm(b, dim=-1)
|
||||
b_star_norms = LA.vector_norm(b_star, dim=-1)
|
||||
r_norms = LA.vector_norm(r, dim=-1)
|
||||
r_star_norms = LA.vector_norm(r_star, dim=-1)
|
||||
|
||||
residuals = (
|
||||
torch.cat(
|
||||
[
|
||||
self.good_residuals,
|
||||
self.bad_residuals,
|
||||
],
|
||||
dim=0,
|
||||
)
|
||||
.detach()
|
||||
.cpu()
|
||||
.numpy()
|
||||
)
|
||||
labels = [0] * len(self.good_residuals) + [1] * len(self.bad_residuals)
|
||||
silhouettes = [
|
||||
silhouette_score(residuals[:, layer_index, :], labels)
|
||||
for layer_index in range(len(self.model.get_layers()) + 1)
|
||||
]
|
||||
|
||||
for layer_index in range(1, len(self.model.get_layers()) + 1):
|
||||
table.add_row(
|
||||
f"{layer_index}",
|
||||
f"{g_b_similarities[layer_index].item():.4f}",
|
||||
f"{g_star_b_star_similarities[layer_index].item():.4f}",
|
||||
f"{g_r_similarities[layer_index].item():.4f}",
|
||||
f"{g_star_r_star_similarities[layer_index].item():.4f}",
|
||||
f"{b_r_similarities[layer_index].item():.4f}",
|
||||
f"{b_star_r_star_similarities[layer_index].item():.4f}",
|
||||
f"{g_norms[layer_index].item():.2f}",
|
||||
f"{g_star_norms[layer_index].item():.2f}",
|
||||
f"{b_norms[layer_index].item():.2f}",
|
||||
f"{b_star_norms[layer_index].item():.2f}",
|
||||
f"{r_norms[layer_index].item():.2f}",
|
||||
f"{r_star_norms[layer_index].item():.2f}",
|
||||
f"{silhouettes[layer_index]:.4f}",
|
||||
)
|
||||
|
||||
print()
|
||||
print("[bold]Residual Geometry[/]")
|
||||
print(table)
|
||||
print("[bold]g[/] = mean of residual vectors for good prompts")
|
||||
print("[bold]g*[/] = geometric median of residual vectors for good prompts")
|
||||
print("[bold]b[/] = mean of residual vectors for bad prompts")
|
||||
print("[bold]b*[/] = geometric median of residual vectors for bad prompts")
|
||||
print("[bold]r[/] = residual direction for means (i.e., [bold]b - g[/])")
|
||||
print(
|
||||
"[bold]r*[/] = residual direction for geometric medians (i.e., [bold]b* - g*[/])"
|
||||
)
|
||||
print("[bold]S(x,y)[/] = cosine similarity of [bold]x[/] and [bold]y[/]")
|
||||
print("[bold]|x|[/] = L2 norm of [bold]x[/]")
|
||||
print(
|
||||
"[bold]Silh[/] = Mean silhouette coefficient of residuals for good/bad clusters"
|
||||
)
|
||||
|
||||
def plot_residuals(self):
|
||||
try:
|
||||
import imageio.v3 as iio # ty:ignore[unresolved-import]
|
||||
import matplotlib.pyplot as plt # ty:ignore[unresolved-import]
|
||||
from geom_median.numpy import ( # ty:ignore[unresolved-import]
|
||||
compute_geometric_median,
|
||||
)
|
||||
from pacmap import PaCMAP # ty:ignore[unresolved-import]
|
||||
except ImportError:
|
||||
print()
|
||||
print(
|
||||
(
|
||||
"[red]Research dependencies not found. Plotting residuals requires "
|
||||
"installing Heretic with the optional research feature, i.e., "
|
||||
"using \"pip install -U 'heretic-llm\\[research]'\".[/]"
|
||||
)
|
||||
)
|
||||
return
|
||||
|
||||
LAYER_FRAME_DURATION = 1000
|
||||
N_TRANSITION_FRAMES = 20
|
||||
TRANSITION_FRAME_DURATION = 50
|
||||
|
||||
print()
|
||||
print("Plotting residual vectors...")
|
||||
|
||||
layer_residuals_2d = []
|
||||
pacmap_init = None
|
||||
|
||||
for layer_index in track(
|
||||
range(1, len(self.model.get_layers()) + 1),
|
||||
description="* Computing PaCMAP projections...",
|
||||
):
|
||||
good_residuals = (
|
||||
self.good_residuals[:, layer_index, :].detach().cpu().numpy()
|
||||
)
|
||||
bad_residuals = self.bad_residuals[:, layer_index, :].detach().cpu().numpy()
|
||||
|
||||
residuals = np.vstack((good_residuals, bad_residuals))
|
||||
embedding = PaCMAP(n_components=2, n_neighbors=30)
|
||||
residuals_2d = embedding.fit_transform(residuals, init=pacmap_init)
|
||||
pacmap_init = residuals_2d
|
||||
|
||||
n_good_residuals = good_residuals.shape[0]
|
||||
good_residuals_2d = residuals_2d[:n_good_residuals]
|
||||
bad_residuals_2d = residuals_2d[n_good_residuals:]
|
||||
|
||||
# Important: These are the medians of the 2D-projected residuals,
|
||||
# not the projections of the medians of the residuals.
|
||||
# Their only purpose is to rotate the individual plots
|
||||
# into a consistent orientation. They are not suitable
|
||||
# for being plotted themselves.
|
||||
good_anchor = compute_geometric_median(good_residuals_2d).median
|
||||
bad_anchor = compute_geometric_median(bad_residuals_2d).median
|
||||
|
||||
# Rotate points to make the line connecting the medians horizontal,
|
||||
# with the median of the good residuals on the left.
|
||||
direction = bad_anchor - good_anchor
|
||||
angle = -np.arctan2(direction[1], direction[0])
|
||||
cosine = np.cos(angle)
|
||||
sine = np.sin(angle)
|
||||
rotation_matrix = np.array([[cosine, -sine], [sine, cosine]])
|
||||
residuals_2d = residuals_2d @ rotation_matrix.T
|
||||
|
||||
good_residuals_2d = residuals_2d[:n_good_residuals]
|
||||
bad_residuals_2d = residuals_2d[n_good_residuals:]
|
||||
|
||||
layer_residuals_2d.append((good_residuals_2d, bad_residuals_2d))
|
||||
|
||||
plt.style.use(self.settings.residual_plot_style)
|
||||
|
||||
def plot(
|
||||
image_path: Path,
|
||||
layer_index: int,
|
||||
good_residuals_2d: NDArray,
|
||||
bad_residuals_2d: NDArray,
|
||||
):
|
||||
fig, ax = plt.subplots(figsize=(8, 6))
|
||||
|
||||
ax.scatter(
|
||||
good_residuals_2d[:, 0],
|
||||
good_residuals_2d[:, 1],
|
||||
s=10,
|
||||
c=self.settings.good_prompts.residual_plot_color,
|
||||
alpha=0.5,
|
||||
label=self.settings.good_prompts.residual_plot_label,
|
||||
)
|
||||
ax.scatter(
|
||||
bad_residuals_2d[:, 0],
|
||||
bad_residuals_2d[:, 1],
|
||||
s=10,
|
||||
c=self.settings.bad_prompts.residual_plot_color,
|
||||
alpha=0.5,
|
||||
label=self.settings.bad_prompts.residual_plot_label,
|
||||
)
|
||||
|
||||
ax.set_title(self.settings.residual_plot_title, pad=11)
|
||||
ax.legend(loc="upper right")
|
||||
ax.grid(False)
|
||||
ax.set_xticks([])
|
||||
ax.set_yticks([])
|
||||
|
||||
fig.text(
|
||||
0.018,
|
||||
0.02,
|
||||
self.settings.model,
|
||||
ha="left",
|
||||
va="bottom",
|
||||
fontsize=12,
|
||||
)
|
||||
fig.text(
|
||||
0.982,
|
||||
0.02,
|
||||
f"Layer {layer_index:03}",
|
||||
ha="right",
|
||||
va="bottom",
|
||||
fontsize=12,
|
||||
)
|
||||
|
||||
fig.tight_layout()
|
||||
fig.subplots_adjust(bottom=0.08)
|
||||
|
||||
fig.savefig(image_path, dpi=100)
|
||||
plt.close(fig)
|
||||
|
||||
base_path = Path(
|
||||
self.settings.residual_plot_path
|
||||
) / self.settings.model.replace(
|
||||
"/",
|
||||
"_",
|
||||
).replace(
|
||||
"\\",
|
||||
"_",
|
||||
)
|
||||
|
||||
base_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
images = []
|
||||
durations = []
|
||||
|
||||
for layer_index, (
|
||||
good_residuals_2d,
|
||||
bad_residuals_2d,
|
||||
) in enumerate(
|
||||
track(
|
||||
layer_residuals_2d,
|
||||
description="* Generating plots...",
|
||||
),
|
||||
1,
|
||||
):
|
||||
image_path = base_path / f"layer_{layer_index:03}.png"
|
||||
|
||||
plot(image_path, layer_index, good_residuals_2d, bad_residuals_2d)
|
||||
|
||||
images.append(iio.imread(image_path))
|
||||
durations.append(LAYER_FRAME_DURATION)
|
||||
|
||||
if layer_index < len(layer_residuals_2d):
|
||||
# The first frame of the transition is the layer frame created above.
|
||||
# The last frame is the next layer frame, created in the next iteration of the outer loop.
|
||||
# The following are the intermediate frames.
|
||||
# There are a total of N_TRANSITION_FRAMES frame changes in the transition.
|
||||
for frame_index in range(1, N_TRANSITION_FRAMES):
|
||||
image_path = (
|
||||
base_path / f"layer_{layer_index:03}_frame_{frame_index:03}.png"
|
||||
)
|
||||
|
||||
progress = frame_index / N_TRANSITION_FRAMES
|
||||
|
||||
good_residuals_2d_interpolated = good_residuals_2d + progress * (
|
||||
layer_residuals_2d[layer_index][0] - good_residuals_2d
|
||||
)
|
||||
bad_residuals_2d_interpolated = bad_residuals_2d + progress * (
|
||||
layer_residuals_2d[layer_index][1] - bad_residuals_2d
|
||||
)
|
||||
|
||||
plot(
|
||||
image_path,
|
||||
layer_index,
|
||||
good_residuals_2d_interpolated,
|
||||
bad_residuals_2d_interpolated,
|
||||
)
|
||||
|
||||
images.append(iio.imread(image_path))
|
||||
durations.append(TRANSITION_FRAME_DURATION)
|
||||
|
||||
# Delete the image file containing the animation frame.
|
||||
# We have already read its contents and it serves no purpose
|
||||
# other than building the animation.
|
||||
image_path.unlink()
|
||||
|
||||
print("* Generating animation...")
|
||||
|
||||
iio.imwrite(
|
||||
base_path / "animation.gif",
|
||||
images,
|
||||
duration=durations,
|
||||
loop=0,
|
||||
)
|
||||
|
||||
print(f"* Plots saved to [bold]{base_path.resolve()}[/].")
|
||||
+57
-111
@@ -32,13 +32,6 @@ class QuantizationMethod(str, Enum):
|
||||
BNB_4BIT = "bnb_4bit"
|
||||
|
||||
|
||||
class RowNormalization(str, Enum):
|
||||
NONE = "none"
|
||||
PRE = "pre"
|
||||
# POST = "post" # Theoretically possible, but provides no advantage.
|
||||
FULL = "full"
|
||||
|
||||
|
||||
class ExportStrategy(str, Enum):
|
||||
MERGE = "merge"
|
||||
ADAPTER = "adapter"
|
||||
@@ -79,18 +72,6 @@ class DatasetSpecification(BaseModel):
|
||||
description="System prompt to use with the prompts (overrides global system prompt if set).",
|
||||
)
|
||||
|
||||
residual_plot_label: str | None = Field(
|
||||
default=None,
|
||||
description="Label to use for the dataset in plots of residual vectors.",
|
||||
exclude=True,
|
||||
)
|
||||
|
||||
residual_plot_color: str | None = Field(
|
||||
default=None,
|
||||
description="Matplotlib color to use for the dataset in plots of residual vectors.",
|
||||
exclude=True,
|
||||
)
|
||||
|
||||
|
||||
class ScorerConfig(BaseModel):
|
||||
"""
|
||||
@@ -142,6 +123,48 @@ class ScorerConfig(BaseModel):
|
||||
return value
|
||||
|
||||
|
||||
class ModifierConfig(BaseModel):
|
||||
"""
|
||||
Configuration for a modifier plugin.
|
||||
|
||||
TOML format:
|
||||
- { plugin = "<plugin>", instance_name = "<optional>" }
|
||||
"""
|
||||
|
||||
plugin: str = Field(
|
||||
description=(
|
||||
"Plugin to load. Either a file path with class name "
|
||||
"(`path/to/plugin.py:ClassName`) or a fully-qualified import path "
|
||||
"(`module.submodule.ClassName`)."
|
||||
),
|
||||
)
|
||||
|
||||
instance_name: str | None = Field(
|
||||
default=None,
|
||||
description=(
|
||||
"Optional name to distinguish multiple instances of the same plugin class. "
|
||||
"Instance-specific settings live under `[modifier.<ClassName>_<instance_name>]`."
|
||||
),
|
||||
)
|
||||
|
||||
@field_validator("instance_name")
|
||||
@classmethod
|
||||
def validate_instance_name(cls, value: str | None) -> str | None:
|
||||
if value is None:
|
||||
return value
|
||||
|
||||
if not value.strip():
|
||||
raise ValueError("cannot be empty or whitespace")
|
||||
|
||||
if "." in value:
|
||||
raise ValueError("'.' is not allowed")
|
||||
|
||||
if any(char.isspace() for char in value):
|
||||
raise ValueError("whitespace is not allowed")
|
||||
|
||||
return value
|
||||
|
||||
|
||||
class BenchmarkSpecification(BaseModel):
|
||||
task: str = Field(
|
||||
description="Task ID of the benchmark in the Language Model Evaluation Harness."
|
||||
@@ -304,38 +327,8 @@ class Settings(BaseSettings):
|
||||
exclude=True,
|
||||
)
|
||||
|
||||
print_residual_geometry: bool = Field(
|
||||
default=False,
|
||||
description="Whether to print detailed information about residuals and residual directions.",
|
||||
exclude=True,
|
||||
)
|
||||
|
||||
plot_residuals: bool = Field(
|
||||
default=False,
|
||||
description="Whether to generate plots showing PaCMAP projections of residual vectors.",
|
||||
exclude=True,
|
||||
)
|
||||
|
||||
residual_plot_path: str = Field(
|
||||
default="plots",
|
||||
description="Base path to save plots of residual vectors to.",
|
||||
exclude=True,
|
||||
)
|
||||
|
||||
residual_plot_title: str = Field(
|
||||
default='PaCMAP Projection of Residual Vectors for "Harmless" and "Harmful" Prompts',
|
||||
description="Title placed above plots of residual vectors.",
|
||||
exclude=True,
|
||||
)
|
||||
|
||||
residual_plot_style: str = Field(
|
||||
default="dark_background",
|
||||
description="Matplotlib style sheet to use for plots of residual vectors.",
|
||||
exclude=True,
|
||||
)
|
||||
|
||||
scorers: list[ScorerConfig] = Field(
|
||||
default_factory=lambda: [
|
||||
default=[
|
||||
ScorerConfig(
|
||||
plugin="heretic.scorers.keyword_rate.KeywordRate",
|
||||
optimization="minimize",
|
||||
@@ -346,48 +339,23 @@ class Settings(BaseSettings):
|
||||
),
|
||||
],
|
||||
description=(
|
||||
"List of scorer plugin configs. Each entry is an object"
|
||||
" { plugin = <plugin>, optimization = <optimization>, instance_name = <optional> }."
|
||||
" <optimization> is one of 'minimize', 'maximize', 'none' (do not optimize)."
|
||||
"List of scorer plugin configs. Each entry is an object "
|
||||
"{ plugin = <plugin>, optimization = <optimization>, instance_name = <optional> }. "
|
||||
'<optimization> is one of "minimize", "maximize", or "none" (do not optimize).'
|
||||
),
|
||||
)
|
||||
|
||||
orthogonalize_direction: bool = Field(
|
||||
default=True,
|
||||
modifiers: list[ModifierConfig] = Field(
|
||||
default=[
|
||||
ModifierConfig(
|
||||
plugin="heretic.modifiers.abliteration.Abliteration",
|
||||
),
|
||||
],
|
||||
description=(
|
||||
"Whether to adjust the residual directions so that only the component that is "
|
||||
"orthogonal to the good direction is subtracted during abliteration."
|
||||
),
|
||||
)
|
||||
|
||||
row_normalization: RowNormalization = Field(
|
||||
default=RowNormalization.FULL,
|
||||
description=(
|
||||
"How to apply row normalization of the weights. Options: "
|
||||
'"none" (no normalization), '
|
||||
'"pre" (compute LoRA adapter relative to row-normalized weights), '
|
||||
'"full" (like "pre", but renormalizes to preserve original row magnitudes).'
|
||||
),
|
||||
)
|
||||
|
||||
full_normalization_lora_rank: PositiveInt = Field(
|
||||
default=3,
|
||||
description=(
|
||||
'The rank of the LoRA adapter to use when "full" row normalization is used. '
|
||||
"Row magnitude preservation is approximate due to non-linear effects, "
|
||||
"and this determines the rank of that approximation. Higher ranks produce "
|
||||
"larger output files and may slow down evaluation."
|
||||
),
|
||||
)
|
||||
|
||||
winsorization_quantile: float = Field(
|
||||
default=1.0,
|
||||
description=(
|
||||
"The symmetric winsorization to apply to the per-prompt, per-layer residual vectors, "
|
||||
"expressed as the quantile to clamp to (between 0 and 1). Disabled by default. "
|
||||
'This can tame so-called "massive activations" that occur in some models. '
|
||||
"Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values "
|
||||
"of the components, then clamps the magnitudes of all components to that quantile."
|
||||
"List of modifier plugin configs. Each entry is an object "
|
||||
"{ plugin = <plugin>, instance_name = <optional> }. "
|
||||
"Note that only a single modifier can currently be applied, "
|
||||
"and this list must contain exactly one entry."
|
||||
),
|
||||
)
|
||||
|
||||
@@ -539,31 +507,9 @@ class Settings(BaseSettings):
|
||||
description="System prompt to use when prompting the model.",
|
||||
)
|
||||
|
||||
good_prompts: DatasetSpecification = Field(
|
||||
default=DatasetSpecification(
|
||||
dataset="mlabonne/harmless_alpaca",
|
||||
split="train[:400]",
|
||||
column="text",
|
||||
residual_plot_label='"Harmless" prompts',
|
||||
residual_plot_color="royalblue",
|
||||
),
|
||||
description="Dataset of prompts that tend to not result in refusals (used for calculating refusal directions).",
|
||||
)
|
||||
|
||||
bad_prompts: DatasetSpecification = Field(
|
||||
default=DatasetSpecification(
|
||||
dataset="mlabonne/harmful_behaviors",
|
||||
split="train[:400]",
|
||||
column="text",
|
||||
residual_plot_label='"Harmful" prompts',
|
||||
residual_plot_color="darkorange",
|
||||
),
|
||||
description="Dataset of prompts that tend to result in refusals (used for calculating refusal directions).",
|
||||
)
|
||||
|
||||
# We intentionally allow extra keys so users can provide plugin-specific
|
||||
# configuration in TOML tables like `[scorer.KeywordRate]` which are later
|
||||
# consumed via `settings.model_extra` (see `Evaluator._get_plugin_namespace`).
|
||||
# consumed via `settings.model_extra` (see `plugin.get_plugin_namespace`).
|
||||
model_config = SettingsConfigDict(extra="allow")
|
||||
|
||||
@classmethod
|
||||
|
||||
@@ -9,9 +9,9 @@ from pydantic import BaseModel
|
||||
|
||||
from .config import DatasetSpecification, ScorerConfig, Settings
|
||||
from .model import Model
|
||||
from .plugin import get_plugin_namespace, is_builtin_plugin, load_plugin
|
||||
from .scorer import Context, Score, Scorer
|
||||
from .utils import deep_merge_dicts, parse_study_direction, print
|
||||
from .plugin import Context, is_builtin_plugin, load_plugin
|
||||
from .scorer import Score, Scorer
|
||||
from .utils import parse_study_direction, print
|
||||
|
||||
|
||||
@dataclass
|
||||
@@ -63,14 +63,16 @@ class Evaluator:
|
||||
scorer_cls.validate_contract()
|
||||
|
||||
print(
|
||||
f"* Loaded: [bold]{scorer_cls.__name__} {'- ' + config.instance_name if config.instance_name else ''}[/bold]"
|
||||
f"* Loaded: [bold]{scorer_cls.__name__}{' - ' + config.instance_name if config.instance_name else ''}[/bold]"
|
||||
)
|
||||
|
||||
# Instantiate scorers.
|
||||
instance_name = config.instance_name or None
|
||||
|
||||
raw_settings = self._get_scorer_settings_raw(
|
||||
scorer_cls=scorer_cls, instance_name=instance_name
|
||||
raw_settings = scorer_cls.get_settings_raw(
|
||||
self.settings.model_extra,
|
||||
"scorer",
|
||||
instance_name,
|
||||
)
|
||||
scorer_settings: BaseModel | None = scorer_cls.validate_settings(
|
||||
raw_settings
|
||||
@@ -117,45 +119,9 @@ class Evaluator:
|
||||
"""
|
||||
specifications = []
|
||||
for entry in self._scorer_entries:
|
||||
if entry.scorer.settings is None:
|
||||
continue
|
||||
for value in dict(entry.scorer.settings).values():
|
||||
if isinstance(value, DatasetSpecification):
|
||||
specifications.append(value)
|
||||
specifications.extend(entry.scorer.get_dataset_specifications())
|
||||
return specifications
|
||||
|
||||
def _get_scorer_settings_raw(
|
||||
self, *, scorer_cls: type[Scorer], instance_name: str | None
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Build the raw settings dict for a scorer class and optional instance.
|
||||
|
||||
Config rules:
|
||||
- Base settings live in `[scorer.ClassName]` (applies to all instances).
|
||||
- Instance overrides live in `[scorer.ClassName_<instance_name>]` (preferred).
|
||||
- Only merge/validate keys that exist in the scorer Settings schema.
|
||||
"""
|
||||
settings_model = scorer_cls.get_settings_model()
|
||||
if settings_model is None:
|
||||
# No settings schema: nothing to merge/validate.
|
||||
return {}
|
||||
|
||||
class_name = scorer_cls.__name__
|
||||
|
||||
namespaces = [f"scorer.{class_name}"]
|
||||
if instance_name:
|
||||
namespaces.append(f"scorer.{class_name}_{instance_name}")
|
||||
|
||||
merged_settings: dict[str, Any] = {}
|
||||
allowed_keys = set(settings_model.model_fields.keys())
|
||||
|
||||
for namespace in namespaces:
|
||||
raw_table = get_plugin_namespace(self.settings.model_extra, namespace)
|
||||
filtered = {k: v for k, v in raw_table.items() if k in allowed_keys}
|
||||
merged_settings = deep_merge_dicts(merged_settings, filtered)
|
||||
|
||||
return merged_settings
|
||||
|
||||
def all_scorers_reproducible(self) -> bool:
|
||||
"""
|
||||
Returns True if all scorers are reproducible,
|
||||
|
||||
+57
-160
@@ -41,7 +41,6 @@ import os
|
||||
import random
|
||||
import time
|
||||
import warnings
|
||||
from dataclasses import asdict
|
||||
from importlib.metadata import version
|
||||
from os.path import commonprefix
|
||||
from pathlib import Path
|
||||
@@ -53,7 +52,6 @@ import numpy as np
|
||||
import optuna
|
||||
import questionary
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import transformers
|
||||
from huggingface_hub import HfApi, ModelCard, ModelCardData
|
||||
from lm_eval.models.huggingface import HFLM
|
||||
@@ -69,10 +67,15 @@ from rich.table import Table
|
||||
from rich.text import Text
|
||||
from rich.traceback import install
|
||||
|
||||
from .analyzer import Analyzer
|
||||
from .config import ExportStrategy, QuantizationMethod
|
||||
from .config import (
|
||||
DatasetSpecification,
|
||||
ExportStrategy,
|
||||
QuantizationMethod,
|
||||
)
|
||||
from .evaluator import Evaluator
|
||||
from .model import AbliterationParameters, Model, get_model_class
|
||||
from .model import Model, get_model_class
|
||||
from .modifier import load_and_init_modifiers
|
||||
from .plugin import Context, is_builtin_plugin
|
||||
from .reproduce import (
|
||||
check_environment,
|
||||
collect_reproducibles,
|
||||
@@ -85,7 +88,6 @@ from .utils import (
|
||||
format_exception,
|
||||
get_file_sha256,
|
||||
get_readme_intro,
|
||||
get_trial_parameters,
|
||||
is_hf_path,
|
||||
load_prompts,
|
||||
print,
|
||||
@@ -243,16 +245,12 @@ def run():
|
||||
# FIXME: "Reproduction"/"reproducibility" name inconsistency!
|
||||
reproduction_information = load_reproduction_information(settings.reproduce)
|
||||
|
||||
# Version 3 is the plugin-era schema, which stores generic scorer
|
||||
# `scores`/`baseline_scores`. It is intentionally NOT compatible with the
|
||||
# pre-plugin v1/v2 schema (hardcoded refusals/KL `metrics`), so those are
|
||||
# rejected rather than silently failing on a missing key later.
|
||||
if reproduction_information["version"] != "3":
|
||||
if reproduction_information["version"] != "4":
|
||||
print(
|
||||
(
|
||||
f"[red]Unsupported file format version: [bold]{reproduction_information['version']}[/].[/] "
|
||||
"This version of Heretic reads version 3 (plugin scorer) reproduce.json files. "
|
||||
"Older files were produced before the scorer-plugin refactor and are not supported. "
|
||||
"This version of Heretic reads version 4 (plugin-based) reproduce.json files. "
|
||||
"Older files were produced before the introduction of the plugin system and are not supported. "
|
||||
"Please install Heretic 1.4 to use these files."
|
||||
)
|
||||
)
|
||||
@@ -412,14 +410,27 @@ def run():
|
||||
print()
|
||||
print_memory_usage()
|
||||
|
||||
# TODO: Introduce a dedicated dataset setting for test prompts.
|
||||
good_prompts_dataset = DatasetSpecification(
|
||||
dataset="mlabonne/harmless_alpaca",
|
||||
split="train[:400]",
|
||||
column="text",
|
||||
)
|
||||
|
||||
bad_prompts_dataset = DatasetSpecification(
|
||||
dataset="mlabonne/harmful_behaviors",
|
||||
split="train[:400]",
|
||||
column="text",
|
||||
)
|
||||
|
||||
print()
|
||||
print(f"Loading good prompts from [bold]{settings.good_prompts.dataset}[/]...")
|
||||
good_prompts = load_prompts(settings, settings.good_prompts)
|
||||
print(f"Loading good prompts from [bold]{good_prompts_dataset.dataset}[/]...")
|
||||
good_prompts = load_prompts(settings, good_prompts_dataset)
|
||||
print(f"* [bold]{len(good_prompts)}[/] prompts loaded")
|
||||
|
||||
print()
|
||||
print(f"Loading bad prompts from [bold]{settings.bad_prompts.dataset}[/]...")
|
||||
bad_prompts = load_prompts(settings, settings.bad_prompts)
|
||||
print(f"Loading bad prompts from [bold]{bad_prompts_dataset.dataset}[/]...")
|
||||
bad_prompts = load_prompts(settings, bad_prompts_dataset)
|
||||
print(f"* [bold]{len(bad_prompts)}[/] prompts loaded")
|
||||
|
||||
if settings.batch_size == 0:
|
||||
@@ -534,53 +545,17 @@ def run():
|
||||
return
|
||||
|
||||
print()
|
||||
print("Calculating per-layer residual directions...")
|
||||
print("Loading and initializing modifiers...")
|
||||
modifier_entries = load_and_init_modifiers(settings, model)
|
||||
|
||||
needs_full_residuals = settings.print_residual_geometry or settings.plot_residuals
|
||||
|
||||
if needs_full_residuals:
|
||||
print("* Obtaining residuals for good prompts...")
|
||||
good_residuals = model.get_residuals_batched(good_prompts)
|
||||
print("* Obtaining residuals for bad prompts...")
|
||||
bad_residuals = model.get_residuals_batched(bad_prompts)
|
||||
|
||||
good_means = good_residuals.mean(dim=0)
|
||||
bad_means = bad_residuals.mean(dim=0)
|
||||
|
||||
analyzer = Analyzer(settings, model, good_residuals, bad_residuals)
|
||||
|
||||
if settings.print_residual_geometry:
|
||||
analyzer.print_residual_geometry()
|
||||
|
||||
if settings.plot_residuals:
|
||||
analyzer.plot_residuals()
|
||||
|
||||
# We don't need the full residuals after computing their means and analyzing geometry.
|
||||
del good_residuals, bad_residuals, analyzer
|
||||
else:
|
||||
print("* Obtaining residual mean for good prompts...")
|
||||
good_means = model.get_residuals_mean(good_prompts)
|
||||
print("* Obtaining residual mean for bad prompts...")
|
||||
bad_means = model.get_residuals_mean(bad_prompts)
|
||||
|
||||
residual_directions = F.normalize(bad_means - good_means, p=2, dim=1)
|
||||
|
||||
if settings.orthogonalize_direction:
|
||||
# Implements https://huggingface.co/blog/grimjim/projected-abliteration
|
||||
# Adjust the residual directions so that only the component that is
|
||||
# orthogonal to the good direction is subtracted during abliteration.
|
||||
good_directions = F.normalize(good_means, p=2, dim=1)
|
||||
projection_vector = torch.sum(residual_directions * good_directions, dim=1)
|
||||
residual_directions = (
|
||||
residual_directions - projection_vector.unsqueeze(1) * good_directions
|
||||
)
|
||||
residual_directions = F.normalize(residual_directions, p=2, dim=1)
|
||||
del good_directions, projection_vector
|
||||
|
||||
del good_means, bad_means
|
||||
# `load_and_init_modifiers` currently guarantees that the returned list has exactly one element.
|
||||
# This may change in the future when support for multiple modifiers is implemented.
|
||||
modifier_entry = modifier_entries[0]
|
||||
modifier = modifier_entry.modifier
|
||||
modifier_name = modifier_entry.name
|
||||
|
||||
# Clear cache before starting the optimization study.
|
||||
# This should free up memory from the objects released with the del statements above.
|
||||
# This should free up memory from temporary objects created while initializing modifiers.
|
||||
empty_cache()
|
||||
|
||||
trial_index = 0
|
||||
@@ -592,95 +567,21 @@ def run():
|
||||
trial_index += 1
|
||||
trial.set_user_attr("index", trial_index)
|
||||
|
||||
direction_scope = trial.suggest_categorical(
|
||||
"direction_scope",
|
||||
[
|
||||
"global",
|
||||
"per layer",
|
||||
],
|
||||
)
|
||||
|
||||
last_layer_index = len(model.get_layers()) - 1
|
||||
|
||||
# Discrimination between "harmful" and "harmless" inputs is usually strongest
|
||||
# in layers slightly past the midpoint of the layer stack. See the original
|
||||
# abliteration paper (https://arxiv.org/abs/2406.11717) for a deeper analysis.
|
||||
#
|
||||
# Note that we always sample this parameter even though we only need it for
|
||||
# the "global" direction scope. The reason is that multivariate TPE doesn't
|
||||
# work with conditional or variable-range parameters.
|
||||
direction_index = trial.suggest_float(
|
||||
"direction_index",
|
||||
0.4 * last_layer_index,
|
||||
0.9 * last_layer_index,
|
||||
)
|
||||
|
||||
if direction_scope == "per layer":
|
||||
direction_index = None
|
||||
|
||||
parameters = {}
|
||||
|
||||
for component in model.get_abliterable_components():
|
||||
# The parameter ranges are based on experiments with various models
|
||||
# and much wider ranges. They are not set in stone and might have to be
|
||||
# adjusted for future models.
|
||||
#
|
||||
# The MLP gets a negative lower bound that is then clamped to 0, so the
|
||||
# optimizer can fully disable its ablation. The clamp puts a positive
|
||||
# probability mass on exactly 0 (the continuous sampler would otherwise
|
||||
# reach 0 with probability zero). Ablating the MLP is often unnecessary for
|
||||
# removing refusals and tends to damage model intelligence more than
|
||||
# ablating the attention output, so on many models the optimum is to leave
|
||||
# it (mostly) untouched. See issue #202.
|
||||
max_weight_lower_bound = -0.25 if component == "mlp.down_proj" else 0.8
|
||||
max_weight = max(
|
||||
0.0,
|
||||
trial.suggest_float(
|
||||
f"{component}.max_weight",
|
||||
max_weight_lower_bound,
|
||||
1.5,
|
||||
),
|
||||
)
|
||||
max_weight_position = trial.suggest_float(
|
||||
f"{component}.max_weight_position",
|
||||
0.6 * last_layer_index,
|
||||
1.0 * last_layer_index,
|
||||
)
|
||||
# For sampling purposes, min_weight is expressed as a fraction of max_weight,
|
||||
# again because multivariate TPE doesn't support variable-range parameters.
|
||||
# The value is transformed into the actual min_weight value below.
|
||||
min_weight = trial.suggest_float(
|
||||
f"{component}.min_weight",
|
||||
0.0,
|
||||
1.0,
|
||||
)
|
||||
min_weight_distance = trial.suggest_float(
|
||||
f"{component}.min_weight_distance",
|
||||
1.0,
|
||||
max(0.6 * last_layer_index, 1.0),
|
||||
)
|
||||
|
||||
parameters[component] = AbliterationParameters(
|
||||
max_weight=max_weight,
|
||||
max_weight_position=max_weight_position,
|
||||
min_weight=(min_weight * max_weight),
|
||||
min_weight_distance=min_weight_distance,
|
||||
)
|
||||
|
||||
trial.set_user_attr("direction_index", direction_index)
|
||||
trial.set_user_attr("parameters", {k: asdict(v) for k, v in parameters.items()})
|
||||
ctx = Context(settings=settings, model=model)
|
||||
parameters = modifier.suggest_parameters(ctx, trial)
|
||||
trial.set_user_attr("parameters", parameters.to_dict())
|
||||
|
||||
print()
|
||||
print(
|
||||
f"[magenta]Running trial [bold]{trial_index}[/] of [bold]{settings.n_trials}[/]...[/]"
|
||||
)
|
||||
print("* Parameters:")
|
||||
for name, value in get_trial_parameters(trial).items():
|
||||
for name, value in modifier.render_trial_parameters(trial).items():
|
||||
print(f" * {name} = [bold]{value}[/]")
|
||||
print("* Resetting model...")
|
||||
model.reset_model()
|
||||
print("* Abliterating...")
|
||||
model.abliterate(residual_directions, direction_index, parameters)
|
||||
modifier.reset_model(ctx)
|
||||
print(f"* Modifying model ({modifier_name})...")
|
||||
modifier.modify_model(ctx, parameters)
|
||||
print("* Evaluating...")
|
||||
scores = evaluator.get_scores()
|
||||
objective_values = evaluator.get_objective_values(scores)
|
||||
@@ -834,13 +735,10 @@ def run():
|
||||
trial_loop_active = False
|
||||
|
||||
if reproduction_mode:
|
||||
parameters = reproduction_information["parameters"]
|
||||
|
||||
trial = create_trial(
|
||||
values=[],
|
||||
user_attrs={
|
||||
"direction_index": parameters["direction_index"],
|
||||
"parameters": parameters["abliteration_parameters"],
|
||||
"parameters": reproduction_information["parameters"],
|
||||
"scores": reproduction_information["scores"],
|
||||
},
|
||||
)
|
||||
@@ -910,24 +808,21 @@ def run():
|
||||
)
|
||||
|
||||
print("* Parameters:")
|
||||
for name, value in get_trial_parameters(trial).items():
|
||||
for name, value in modifier.render_trial_parameters(trial).items():
|
||||
print(f" * {name} = [bold]{value}[/]")
|
||||
|
||||
# Per https://github.com/huggingface/peft/issues/868#issuecomment-1820642893
|
||||
# once a LoRA is merged it's expected to be empty. Provide a utility function
|
||||
# to restore the previous LoRA-ified state.
|
||||
def reset_trial_model():
|
||||
ctx = Context(settings=settings, model=model)
|
||||
print("* Resetting model...")
|
||||
model.reset_model()
|
||||
print("* Abliterating...")
|
||||
model.abliterate(
|
||||
residual_directions,
|
||||
trial.user_attrs["direction_index"],
|
||||
{
|
||||
k: AbliterationParameters(**v)
|
||||
for k, v in trial.user_attrs["parameters"].items()
|
||||
},
|
||||
modifier.reset_model(ctx)
|
||||
print(f"* Modifying model ({modifier_name})...")
|
||||
parameters = modifier.parameters_class.from_dict(
|
||||
trial.user_attrs["parameters"]
|
||||
)
|
||||
modifier.modify_model(ctx, parameters)
|
||||
|
||||
reset_trial_model()
|
||||
|
||||
@@ -1110,12 +1005,11 @@ def run():
|
||||
# are available on the Hugging Face Hub (not local paths),
|
||||
# that all datasets are pinned to a commit (an unpinned
|
||||
# dataset was likely loaded from a local cache), and that
|
||||
# only built-in scorer plugins are used (external plugins
|
||||
# cannot be resolved when reproducing).
|
||||
# only built-in plugins are used (external plugins cannot
|
||||
# be resolved when reproducing).
|
||||
dataset_specifications = [
|
||||
settings.good_prompts,
|
||||
settings.bad_prompts,
|
||||
*evaluator.get_dataset_specifications(),
|
||||
*modifier.get_dataset_specifications(),
|
||||
]
|
||||
is_reproducible = (
|
||||
is_hf_path(settings.model)
|
||||
@@ -1126,6 +1020,8 @@ def run():
|
||||
)
|
||||
and evaluator.all_scorers_reproducible()
|
||||
and evaluator.all_scorers_builtin()
|
||||
and modifier.reproducible
|
||||
and is_builtin_plugin(modifier_entry.config.plugin)
|
||||
and not reproduction_mode
|
||||
)
|
||||
|
||||
@@ -1225,6 +1121,7 @@ def run():
|
||||
card.text = (
|
||||
get_readme_intro(
|
||||
settings,
|
||||
modifier,
|
||||
trial,
|
||||
reproducibility_information != "none",
|
||||
)
|
||||
|
||||
+36
-204
@@ -1,17 +1,11 @@
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
# Copyright (C) 2025-2026 Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
|
||||
|
||||
import math
|
||||
from contextlib import suppress
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Type, cast
|
||||
|
||||
import bitsandbytes as bnb
|
||||
import torch
|
||||
import torch.linalg as LA
|
||||
import torch.nn.functional as F
|
||||
from peft import LoraConfig, PeftModel, get_peft_model
|
||||
from peft.tuners.lora.layer import Linear
|
||||
from torch import FloatTensor, LongTensor, Tensor
|
||||
from torch.nn import Module, ModuleList
|
||||
from transformers import (
|
||||
@@ -31,7 +25,7 @@ from transformers.generation import (
|
||||
GenerateDecoderOnlyOutput, # ty:ignore[possibly-missing-import]
|
||||
)
|
||||
|
||||
from .config import QuantizationMethod, RowNormalization, Settings
|
||||
from .config import QuantizationMethod, Settings
|
||||
from .system import empty_cache
|
||||
from .utils import Prompt, batchify, format_exception, print
|
||||
|
||||
@@ -47,14 +41,6 @@ def get_model_class(
|
||||
return AutoModelForCausalLM
|
||||
|
||||
|
||||
@dataclass
|
||||
class AbliterationParameters:
|
||||
max_weight: float
|
||||
max_weight_position: float
|
||||
min_weight: float
|
||||
min_weight_distance: float
|
||||
|
||||
|
||||
class Model:
|
||||
model: PreTrainedModel | PeftModel
|
||||
tokenizer: PreTrainedTokenizerBase
|
||||
@@ -168,11 +154,6 @@ class Model:
|
||||
if self.model is None:
|
||||
raise Exception("Failed to load model with all configured dtypes.")
|
||||
|
||||
self._apply_lora()
|
||||
|
||||
# LoRA B matrices are initialized to zero by default in PEFT,
|
||||
# so we don't need to do anything manually.
|
||||
|
||||
print(f"* Transformer model with [bold]{len(self.get_layers())}[/] layers")
|
||||
|
||||
all_components = {}
|
||||
@@ -186,7 +167,7 @@ class Model:
|
||||
for component, count in all_components.items():
|
||||
print(f" * [bold]{component}[/]: [bold]{count}[/] modules total")
|
||||
|
||||
def _apply_lora(self):
|
||||
def apply_lora(self, lora_rank: int):
|
||||
# Guard against calling this method at the wrong time.
|
||||
assert isinstance(self.model, PreTrainedModel)
|
||||
|
||||
@@ -211,13 +192,6 @@ class Model:
|
||||
|
||||
target_modules = sorted(target_modules_set)
|
||||
|
||||
if self.settings.row_normalization != RowNormalization.FULL:
|
||||
# Rank 1 is sufficient for directional ablation without renormalization.
|
||||
lora_rank = 1
|
||||
else:
|
||||
# Row magnitude preservation introduces nonlinear effects.
|
||||
lora_rank = self.settings.full_normalization_lora_rank
|
||||
|
||||
self.peft_config = LoraConfig(
|
||||
r=lora_rank,
|
||||
target_modules=target_modules,
|
||||
@@ -233,11 +207,6 @@ class Model:
|
||||
# so the result is a PeftModel rather than a PeftMixedModel.
|
||||
self.model = cast(PeftModel, get_peft_model(self.model, self.peft_config))
|
||||
|
||||
display_targets = sorted({name.rsplit(".", 1)[-1] for name in target_modules})
|
||||
print(
|
||||
f"* LoRA adapters initialized (target types: {', '.join(display_targets)})"
|
||||
)
|
||||
|
||||
def _get_quantization_config(self, dtype: str) -> BitsAndBytesConfig | None:
|
||||
"""
|
||||
Creates quantization config based on settings.
|
||||
@@ -312,7 +281,7 @@ class Model:
|
||||
self.needs_reload = True
|
||||
return merged_model
|
||||
|
||||
def reset_model(self):
|
||||
def reset_model(self) -> bool:
|
||||
"""
|
||||
Resets the model to a clean state for the next trial or evaluation.
|
||||
|
||||
@@ -321,6 +290,8 @@ class Model:
|
||||
resets LoRA adapter weights to zero (identity transformation).
|
||||
- Slow path: If switching models or after merge_and_unload(),
|
||||
performs full model reload with quantization config.
|
||||
|
||||
Returns True if the fast path was taken.
|
||||
"""
|
||||
|
||||
# If a prior model load was interrupted/cancelled mid-process, self.model will be None.
|
||||
@@ -333,7 +304,7 @@ class Model:
|
||||
for name, module in self.model.named_modules():
|
||||
if "lora_B" in name and hasattr(module, "weight"):
|
||||
torch.nn.init.zeros_(module.weight)
|
||||
return
|
||||
return True
|
||||
|
||||
# Purge existing model object from memory to make space.
|
||||
self.model = None # ty:ignore[invalid-assignment]
|
||||
@@ -360,10 +331,10 @@ class Model:
|
||||
**extra_kwargs,
|
||||
)
|
||||
|
||||
self._apply_lora()
|
||||
|
||||
self.needs_reload = False
|
||||
|
||||
return False
|
||||
|
||||
def get_layers(self) -> ModuleList:
|
||||
model = self.model
|
||||
|
||||
@@ -458,166 +429,6 @@ class Model:
|
||||
|
||||
return sorted(components)
|
||||
|
||||
def abliterate(
|
||||
self,
|
||||
residual_directions: Tensor,
|
||||
direction_index: float | None,
|
||||
parameters: dict[str, AbliterationParameters],
|
||||
):
|
||||
if direction_index is None:
|
||||
residual_direction = None
|
||||
else:
|
||||
# The index must be shifted by 1 because the first element
|
||||
# of residual_directions is the direction for the embeddings.
|
||||
weight, index = math.modf(direction_index + 1)
|
||||
residual_direction = F.normalize(
|
||||
residual_directions[int(index)].lerp(
|
||||
residual_directions[int(index) + 1],
|
||||
weight,
|
||||
),
|
||||
p=2,
|
||||
dim=0,
|
||||
)
|
||||
|
||||
# Note that some implementations of abliteration also orthogonalize
|
||||
# the embedding matrix, but it's unclear if that has any benefits.
|
||||
for layer_index in range(len(self.get_layers())):
|
||||
for component, modules in self.get_layer_modules(layer_index).items():
|
||||
params = parameters[component]
|
||||
|
||||
# Type inference fails here for some reason.
|
||||
distance = cast(float, abs(layer_index - params.max_weight_position))
|
||||
|
||||
# Don't orthogonalize layers that are more than
|
||||
# min_weight_distance away from max_weight_position.
|
||||
if distance > params.min_weight_distance:
|
||||
continue
|
||||
|
||||
# Interpolate linearly between max_weight and min_weight
|
||||
# over min_weight_distance.
|
||||
weight = params.max_weight + (distance / params.min_weight_distance) * (
|
||||
params.min_weight - params.max_weight
|
||||
)
|
||||
|
||||
# A weight of 0 disables this component's ablation. reset_model() has
|
||||
# already left the adapter at identity, so abort before the otherwise
|
||||
# wasteful decomposition (which would also be operating on a zero matrix).
|
||||
if weight == 0:
|
||||
continue
|
||||
|
||||
if residual_direction is None:
|
||||
# The index must be shifted by 1 because the first element
|
||||
# of residual_directions is the direction for the embeddings.
|
||||
layer_residual_direction = residual_directions[layer_index + 1]
|
||||
else:
|
||||
layer_residual_direction = residual_direction
|
||||
|
||||
for module in modules:
|
||||
# FIXME: This cast is potentially invalid, because the program logic
|
||||
# does not guarantee that the module is of type Linear, and in fact
|
||||
# the retrieved modules might not conform to the interface assumed
|
||||
# below (though they do in practice). However, this is difficult
|
||||
# to fix cleanly, because get_layer_modules is called twice on
|
||||
# different model configurations, and PEFT employs different
|
||||
# module types depending on the chosen quantization.
|
||||
module = cast(Linear, module)
|
||||
|
||||
# LoRA abliteration: delta W = -lambda * v * (v^T W)
|
||||
# lora_B = -lambda * v
|
||||
# lora_A = v^T W
|
||||
|
||||
# Use the FP32 residual direction directly (no downcast/upcast)
|
||||
# and move to the correct device.
|
||||
v = layer_residual_direction.to(module.weight.device)
|
||||
|
||||
# Get W (dequantize if necessary).
|
||||
#
|
||||
# FIXME: This cast is valid only under the assumption that the original
|
||||
# module wrapped by the LoRA adapter has a weight attribute.
|
||||
# See the comment above for why this is currently not guaranteed.
|
||||
base_weight = cast(Tensor, module.base_layer.weight)
|
||||
quant_state = getattr(base_weight, "quant_state", None)
|
||||
|
||||
if quant_state is None:
|
||||
W = base_weight.to(torch.float32)
|
||||
else:
|
||||
# 4-bit quantization.
|
||||
# This cast is always valid. Type inference fails here because the
|
||||
# bnb.functional module is not found by ty for some reason.
|
||||
W = cast(
|
||||
Tensor,
|
||||
bnb.functional.dequantize_4bit( # ty:ignore[possibly-missing-attribute]
|
||||
base_weight.data,
|
||||
quant_state,
|
||||
).to(torch.float32),
|
||||
)
|
||||
|
||||
# Flatten weight matrix to (out_features, in_features).
|
||||
W = W.view(W.shape[0], -1)
|
||||
|
||||
if self.settings.row_normalization == RowNormalization.FULL:
|
||||
# Keep a reference to the original weight matrix so we can subtract it later.
|
||||
W_org = W
|
||||
|
||||
if self.settings.row_normalization != RowNormalization.NONE:
|
||||
# Get the row norms.
|
||||
W_row_norms = LA.vector_norm(W, dim=1, keepdim=True)
|
||||
# Normalize the weight matrix along the rows.
|
||||
W = F.normalize(W, p=2, dim=1)
|
||||
|
||||
# Calculate lora_A = v^T W
|
||||
# v is (d_out,), W is (d_out, d_in)
|
||||
# v @ W -> (d_in,)
|
||||
lora_A = (v @ W).view(1, -1)
|
||||
|
||||
# Calculate lora_B = -weight * v
|
||||
# v is (d_out,)
|
||||
lora_B = (-weight * v).view(-1, 1)
|
||||
|
||||
if self.settings.row_normalization == RowNormalization.PRE:
|
||||
# Make the LoRA adapter apply to the original weight matrix.
|
||||
lora_B = W_row_norms * lora_B
|
||||
elif self.settings.row_normalization == RowNormalization.FULL:
|
||||
# Approximates https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration
|
||||
W = W + lora_B @ lora_A
|
||||
# Normalize the adjusted weight matrix along the rows.
|
||||
W = F.normalize(W, p=2, dim=1)
|
||||
# Restore the original row norms of the weight matrix.
|
||||
W = W * W_row_norms
|
||||
# Subtract the original matrix to turn W into a delta.
|
||||
W = W - W_org
|
||||
# Use a low-rank SVD to get an approximation of the matrix.
|
||||
r = self.peft_config.r
|
||||
|
||||
# svd_lowrank is randomized:
|
||||
# https://github.com/pytorch/pytorch/blob/20919052303c0b5ba87f8bf7e19237dc33ab09d3/torch/_lowrank.py#L108-L109
|
||||
# Reseed immediately before the call so restoring a trial is independent of RNG history.
|
||||
torch.manual_seed(self.settings.seed)
|
||||
# "It's safe to call this function if CUDA is not available;
|
||||
# in that case, it is silently ignored."
|
||||
torch.cuda.manual_seed_all(self.settings.seed) # ty:ignore[invalid-argument-type]
|
||||
U, S, Vh = torch.svd_lowrank(W, q=2 * r + 4, niter=6)
|
||||
|
||||
# Truncate it to the part we want to store in the LoRA adapter.
|
||||
# Note: svd_lowrank actually returns V, so transpose it to get Vh.
|
||||
U = U[:, :r]
|
||||
S = S[:r]
|
||||
Vh = Vh[:, :r].T
|
||||
# Transfer it into the LoRA adapter components. Split the singular values
|
||||
# evenly between the two components to keep their norms balanced and avoid
|
||||
# potential issues with numerical stability.
|
||||
sqrt_S = torch.sqrt(S)
|
||||
lora_B = U @ torch.diag(sqrt_S)
|
||||
lora_A = torch.diag(sqrt_S) @ Vh
|
||||
|
||||
# Assign to adapters. The adapter name is "default", because that's
|
||||
# what PEFT uses when no name is explicitly specified, as above.
|
||||
# These casts are therefore valid.
|
||||
weight_A = cast(Tensor, module.lora_A["default"].weight)
|
||||
weight_B = cast(Tensor, module.lora_B["default"].weight)
|
||||
weight_A.data = lora_A.to(weight_A.dtype)
|
||||
weight_B.data = lora_B.to(weight_B.dtype)
|
||||
|
||||
def generate(
|
||||
self,
|
||||
prompts: list[Prompt],
|
||||
@@ -691,6 +502,7 @@ class Model:
|
||||
skip_special_tokens: bool = False,
|
||||
) -> list[str]:
|
||||
responses = []
|
||||
|
||||
for batch in batchify(prompts, self.settings.batch_size):
|
||||
for response in self.get_responses(
|
||||
batch,
|
||||
@@ -700,7 +512,11 @@ class Model:
|
||||
|
||||
return responses
|
||||
|
||||
def get_residuals(self, prompts: list[Prompt]) -> Tensor:
|
||||
def get_residuals(
|
||||
self,
|
||||
prompts: list[Prompt],
|
||||
winsorization_quantile: float = 1.0,
|
||||
) -> Tensor:
|
||||
# We only generate one token, and we return the residual vectors
|
||||
# at that token position, for each prompt and layer.
|
||||
_, outputs = self.generate(
|
||||
@@ -734,13 +550,13 @@ class Model:
|
||||
# problems during calculations involving residual vectors.
|
||||
residuals = residuals.to(torch.float32)
|
||||
|
||||
if 0 <= self.settings.winsorization_quantile < 1:
|
||||
if 0 <= winsorization_quantile < 1:
|
||||
# Apply symmetric winsorization to each layer of the per-prompt residuals.
|
||||
abs_residuals = torch.abs(residuals)
|
||||
# Get the (prompt, layer, 1) quantiles of the (prompt, layer, component) residuals.
|
||||
thresholds = torch.quantile(
|
||||
abs_residuals,
|
||||
self.settings.winsorization_quantile,
|
||||
winsorization_quantile,
|
||||
dim=2,
|
||||
keepdim=True,
|
||||
)
|
||||
@@ -752,15 +568,28 @@ class Model:
|
||||
|
||||
return residuals
|
||||
|
||||
def get_residuals_batched(self, prompts: list[Prompt]) -> Tensor:
|
||||
def get_residuals_batched(
|
||||
self,
|
||||
prompts: list[Prompt],
|
||||
winsorization_quantile: float = 1.0,
|
||||
) -> Tensor:
|
||||
residuals = []
|
||||
|
||||
for batch in batchify(prompts, self.settings.batch_size):
|
||||
residuals.append(self.get_residuals(batch))
|
||||
residuals.append(
|
||||
self.get_residuals(
|
||||
batch,
|
||||
winsorization_quantile=winsorization_quantile,
|
||||
)
|
||||
)
|
||||
|
||||
return torch.cat(residuals, dim=0)
|
||||
|
||||
def get_residuals_mean(self, prompts: list[Prompt]) -> Tensor:
|
||||
def get_residuals_mean(
|
||||
self,
|
||||
prompts: list[Prompt],
|
||||
winsorization_quantile: float = 1.0,
|
||||
) -> Tensor:
|
||||
if not prompts:
|
||||
raise ValueError("prompts must not be empty")
|
||||
|
||||
@@ -768,7 +597,10 @@ class Model:
|
||||
total_count = 0
|
||||
|
||||
for batch in batchify(prompts, self.settings.batch_size):
|
||||
batch_residuals = self.get_residuals(batch)
|
||||
batch_residuals = self.get_residuals(
|
||||
batch,
|
||||
winsorization_quantile=winsorization_quantile,
|
||||
)
|
||||
|
||||
# Accumulate in high precision on CPU to reduce peak VRAM usage.
|
||||
batch_sum = batch_residuals.sum(dim=0, dtype=torch.float64).cpu()
|
||||
|
||||
+136
-4
@@ -2,16 +2,34 @@
|
||||
# Copyright (C) 2025-2026 Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Generic, TypeVar
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Generic, Protocol, TypeVar, get_args
|
||||
|
||||
from optuna import Trial
|
||||
from optuna.trial import FrozenTrial
|
||||
from pydantic import BaseModel
|
||||
|
||||
from heretic.plugin import Context, Plugin
|
||||
from .config import (
|
||||
ModifierConfig,
|
||||
)
|
||||
from .config import (
|
||||
Settings as HereticSettings,
|
||||
)
|
||||
from .model import Model
|
||||
from .plugin import Context, Plugin, load_plugin
|
||||
from .utils import print
|
||||
|
||||
from .config import Settings as HereticSettings
|
||||
|
||||
Parameters = TypeVar("Parameters")
|
||||
class Serializable(Protocol):
|
||||
def to_dict(self) -> dict[str, Any]: ...
|
||||
|
||||
def to_presentation_dict(self) -> dict[str, str]: ...
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> "Serializable": ...
|
||||
|
||||
|
||||
Parameters = TypeVar("Parameters", bound=Serializable)
|
||||
|
||||
|
||||
class Modifier(Generic[Parameters], Plugin, ABC):
|
||||
@@ -23,6 +41,23 @@ class Modifier(Generic[Parameters], Plugin, ABC):
|
||||
Examples: Standard abliteration, ARA, SOMA, etc.
|
||||
"""
|
||||
|
||||
@property
|
||||
def modifier_name(self) -> str:
|
||||
"""
|
||||
The name of the modifier.
|
||||
This is what shows up in the CLI and Markdown on HF.
|
||||
"""
|
||||
return self.__class__.__name__
|
||||
|
||||
@property
|
||||
def parameters_class(self) -> type[Parameters]:
|
||||
"""
|
||||
The class of the modifier's parameters type.
|
||||
"""
|
||||
base_class = self.__class__.__orig_bases__[0] # ty:ignore[unresolved-attribute]
|
||||
generic_type = get_args(base_class)[0]
|
||||
return generic_type
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
heretic_settings: HereticSettings,
|
||||
@@ -51,3 +86,100 @@ class Modifier(Generic[Parameters], Plugin, ABC):
|
||||
Reset the model (obtainable via `ctx.get_model()`),
|
||||
undoing any changes made by `modify_model`.
|
||||
"""
|
||||
|
||||
def render_trial_parameters(self, trial: Trial | FrozenTrial) -> dict[str, str]:
|
||||
"""
|
||||
Transform the names and values of the modifier's parameters
|
||||
that are contained in the trial's user attributes into a form
|
||||
suitable for presentation.
|
||||
"""
|
||||
return self.parameters_class.from_dict(
|
||||
trial.user_attrs["parameters"]
|
||||
).to_presentation_dict()
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModifierEntry:
|
||||
modifier: Modifier[Any]
|
||||
name: str
|
||||
config: ModifierConfig
|
||||
|
||||
|
||||
def load_and_init_modifiers(
|
||||
settings: HereticSettings,
|
||||
model: Model,
|
||||
) -> list[ModifierEntry]:
|
||||
"""
|
||||
Load and instantiate all configured modifier plugins,
|
||||
then runs their initialization hooks.
|
||||
"""
|
||||
modifier_configs = settings.modifiers
|
||||
if not modifier_configs:
|
||||
raise ValueError("No modifiers configured. Set 'modifiers' in config.toml")
|
||||
if len(modifier_configs) > 1:
|
||||
raise ValueError("Using multiple modifiers is not yet supported")
|
||||
|
||||
modifier_keys: set[str] = set()
|
||||
|
||||
modifier_entries: list[ModifierEntry] = []
|
||||
|
||||
# Resolve plugin classes from names and validate.
|
||||
for config in modifier_configs:
|
||||
modifier_cls = load_plugin(name=config.plugin, base_class=Modifier)
|
||||
modifier_cls.validate_contract()
|
||||
|
||||
print(
|
||||
f"* Loaded: [bold]{modifier_cls.__name__}{' - ' + config.instance_name if config.instance_name else ''}[/bold]"
|
||||
)
|
||||
|
||||
# Instantiate modifiers.
|
||||
instance_name = config.instance_name or None
|
||||
|
||||
raw_settings = modifier_cls.get_settings_raw(
|
||||
settings.model_extra,
|
||||
"modifier",
|
||||
instance_name,
|
||||
)
|
||||
modifier_settings: BaseModel | None = modifier_cls.validate_settings(
|
||||
raw_settings
|
||||
)
|
||||
|
||||
modifier = modifier_cls(
|
||||
heretic_settings=settings,
|
||||
settings=modifier_settings,
|
||||
)
|
||||
|
||||
# External labeling key: ensures multiple instances can coexist.
|
||||
# Uses underscore to match the TOML namespace format (`modifier.<Class>_<instance>`).
|
||||
modifier_key = (
|
||||
modifier_cls.__name__
|
||||
if not instance_name
|
||||
else f"{modifier_cls.__name__}_{instance_name}"
|
||||
)
|
||||
if modifier_key in modifier_keys:
|
||||
raise ValueError(
|
||||
f"Duplicate modifier instance name: {modifier_key}. "
|
||||
"Give each instance a unique `instance_name`."
|
||||
)
|
||||
modifier_keys.add(modifier_key)
|
||||
|
||||
modifier_instance_name = (
|
||||
f"{modifier.modifier_name} - {instance_name}"
|
||||
if instance_name
|
||||
else modifier.modifier_name
|
||||
)
|
||||
modifier_entries.append(
|
||||
ModifierEntry(
|
||||
modifier=modifier,
|
||||
config=config,
|
||||
name=modifier_instance_name,
|
||||
)
|
||||
)
|
||||
|
||||
# Run modifier init hooks.
|
||||
ctx = Context(settings=settings, model=model)
|
||||
|
||||
for entry in modifier_entries:
|
||||
entry.modifier.init(ctx)
|
||||
|
||||
return modifier_entries
|
||||
|
||||
@@ -0,0 +1,473 @@
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
# Copyright (C) 2025-2026 Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
|
||||
|
||||
import math
|
||||
from dataclasses import asdict, dataclass
|
||||
from enum import Enum
|
||||
from typing import Any, cast
|
||||
|
||||
import bitsandbytes as bnb
|
||||
import torch
|
||||
import torch.linalg as LA
|
||||
import torch.nn.functional as F
|
||||
from optuna import Trial
|
||||
from peft.tuners.lora.layer import Linear
|
||||
from pydantic import (
|
||||
BaseModel,
|
||||
Field,
|
||||
PositiveInt,
|
||||
)
|
||||
from torch import Tensor
|
||||
|
||||
from heretic.config import DatasetSpecification
|
||||
from heretic.modifier import Context, Modifier, Serializable
|
||||
from heretic.utils import print
|
||||
|
||||
|
||||
@dataclass
|
||||
class WeightDistribution:
|
||||
max_weight: float
|
||||
max_weight_position: float
|
||||
min_weight: float
|
||||
min_weight_distance: float
|
||||
|
||||
|
||||
@dataclass
|
||||
class Parameters(Serializable):
|
||||
direction_index: float | None
|
||||
weight_distributions: dict[str, WeightDistribution]
|
||||
|
||||
def to_dict(self) -> dict[str, Any]:
|
||||
return asdict(self)
|
||||
|
||||
def to_presentation_dict(self) -> dict[str, str]:
|
||||
parameters = {}
|
||||
|
||||
parameters["direction_index"] = (
|
||||
"per layer"
|
||||
if (self.direction_index is None)
|
||||
else f"{self.direction_index:.2f}"
|
||||
)
|
||||
|
||||
for component, weight_distribution in self.weight_distributions.items():
|
||||
for name, value in asdict(weight_distribution).items():
|
||||
parameters[f"{component}.{name}"] = f"{value:.2f}"
|
||||
|
||||
return parameters
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any]) -> "Serializable":
|
||||
return Parameters(
|
||||
direction_index=data["direction_index"],
|
||||
weight_distributions={
|
||||
component: WeightDistribution(**weight_distribution)
|
||||
for component, weight_distribution in data[
|
||||
"weight_distributions"
|
||||
].items()
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
class RowNormalization(str, Enum):
|
||||
NONE = "none"
|
||||
PRE = "pre"
|
||||
# POST = "post" # Theoretically possible, but provides no advantage.
|
||||
FULL = "full"
|
||||
|
||||
|
||||
class Settings(BaseModel):
|
||||
good_prompts: DatasetSpecification = Field(
|
||||
default=DatasetSpecification(
|
||||
dataset="mlabonne/harmless_alpaca",
|
||||
split="train[:400]",
|
||||
column="text",
|
||||
),
|
||||
description="Dataset of prompts that tend to produce desirable responses.",
|
||||
)
|
||||
|
||||
bad_prompts: DatasetSpecification = Field(
|
||||
default=DatasetSpecification(
|
||||
dataset="mlabonne/harmful_behaviors",
|
||||
split="train[:400]",
|
||||
column="text",
|
||||
),
|
||||
description="Dataset of prompts that tend to produce undesirable responses.",
|
||||
)
|
||||
|
||||
orthogonalize_direction: bool = Field(
|
||||
default=True,
|
||||
description=(
|
||||
"Whether to adjust the residual directions so that only the component that is "
|
||||
"orthogonal to the good direction is subtracted during abliteration."
|
||||
),
|
||||
)
|
||||
|
||||
row_normalization: RowNormalization = Field(
|
||||
default=RowNormalization.FULL,
|
||||
description=(
|
||||
"How to apply row normalization of the weights. Options: "
|
||||
'"none" (no normalization), '
|
||||
'"pre" (compute LoRA adapter relative to row-normalized weights), '
|
||||
'"full" (like "pre", but renormalizes to preserve original row magnitudes).'
|
||||
),
|
||||
)
|
||||
|
||||
full_normalization_lora_rank: PositiveInt = Field(
|
||||
default=3,
|
||||
description=(
|
||||
'The rank of the LoRA adapter to use when "full" row normalization is used. '
|
||||
"Row magnitude preservation is approximate due to non-linear effects, "
|
||||
"and this determines the rank of that approximation. Higher ranks produce "
|
||||
"larger output files and may slow down evaluation."
|
||||
),
|
||||
)
|
||||
|
||||
winsorization_quantile: float = Field(
|
||||
default=1.0,
|
||||
description=(
|
||||
"The symmetric winsorization to apply to the per-prompt, per-layer residual vectors, "
|
||||
"expressed as the quantile to clamp to (between 0 and 1). Disabled by default. "
|
||||
'This can tame so-called "massive activations" that occur in some models. '
|
||||
"Example: winsorization_quantile = 0.95 computes the 0.95-quantile of the absolute values "
|
||||
"of the components, then clamps the magnitudes of all components to that quantile."
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
class Abliteration(Modifier[Parameters]):
|
||||
settings: Settings
|
||||
|
||||
@property
|
||||
def reproducible(self) -> bool:
|
||||
return True
|
||||
|
||||
@property
|
||||
def modifier_name(self) -> str:
|
||||
if (
|
||||
self.settings.orthogonalize_direction
|
||||
and self.settings.row_normalization == RowNormalization.FULL
|
||||
):
|
||||
return "Magnitude-Preserving Orthogonal Ablation (MPOA)"
|
||||
elif self.settings.orthogonalize_direction:
|
||||
return "Projected Abliteration"
|
||||
else:
|
||||
return "Abliteration"
|
||||
|
||||
def init(self, ctx: Context) -> None:
|
||||
model = ctx.get_model()
|
||||
|
||||
print()
|
||||
print(
|
||||
f"Loading good prompts from [bold]{self.settings.good_prompts.dataset}[/]..."
|
||||
)
|
||||
self.good_prompts = ctx.load_prompts(self.settings.good_prompts)
|
||||
print(f"* [bold]{len(self.good_prompts)}[/] prompts loaded")
|
||||
|
||||
print()
|
||||
print(
|
||||
f"Loading bad prompts from [bold]{self.settings.bad_prompts.dataset}[/]..."
|
||||
)
|
||||
self.bad_prompts = ctx.load_prompts(self.settings.bad_prompts)
|
||||
print(f"* [bold]{len(self.bad_prompts)}[/] prompts loaded")
|
||||
|
||||
print()
|
||||
print("Calculating per-layer residual directions...")
|
||||
|
||||
print("* Obtaining residual mean for good prompts...")
|
||||
good_means = model.get_residuals_mean(
|
||||
self.good_prompts,
|
||||
winsorization_quantile=self.settings.winsorization_quantile,
|
||||
)
|
||||
print("* Obtaining residual mean for bad prompts...")
|
||||
bad_means = model.get_residuals_mean(
|
||||
self.bad_prompts,
|
||||
winsorization_quantile=self.settings.winsorization_quantile,
|
||||
)
|
||||
|
||||
self.residual_directions = F.normalize(
|
||||
bad_means - good_means,
|
||||
p=2,
|
||||
dim=1,
|
||||
)
|
||||
|
||||
if self.settings.orthogonalize_direction:
|
||||
# Implements https://huggingface.co/blog/grimjim/projected-abliteration
|
||||
# Adjust the residual directions so that only the component that is
|
||||
# orthogonal to the good direction is subtracted during abliteration.
|
||||
good_directions = F.normalize(
|
||||
good_means,
|
||||
p=2,
|
||||
dim=1,
|
||||
)
|
||||
projection_vector = torch.sum(
|
||||
self.residual_directions * good_directions,
|
||||
dim=1,
|
||||
)
|
||||
self.residual_directions = (
|
||||
self.residual_directions
|
||||
- projection_vector.unsqueeze(1) * good_directions
|
||||
)
|
||||
self.residual_directions = F.normalize(
|
||||
self.residual_directions,
|
||||
p=2,
|
||||
dim=1,
|
||||
)
|
||||
|
||||
if self.settings.row_normalization != RowNormalization.FULL:
|
||||
# Rank 1 is sufficient for directional ablation without renormalization.
|
||||
self.lora_rank = 1
|
||||
else:
|
||||
# Row magnitude preservation introduces nonlinear effects.
|
||||
self.lora_rank = self.settings.full_normalization_lora_rank
|
||||
|
||||
# LoRA B matrices are initialized to zero by default in PEFT,
|
||||
# so we don't need to do anything manually.
|
||||
model.apply_lora(self.lora_rank)
|
||||
|
||||
def suggest_parameters(self, ctx: Context, trial: Trial) -> Parameters:
|
||||
model = ctx.get_model()
|
||||
|
||||
direction_scope = trial.suggest_categorical(
|
||||
"direction_scope",
|
||||
[
|
||||
"global",
|
||||
"per layer",
|
||||
],
|
||||
)
|
||||
|
||||
last_layer_index = len(model.get_layers()) - 1
|
||||
|
||||
# Discrimination between "harmful" and "harmless" inputs is usually strongest
|
||||
# in layers slightly past the midpoint of the layer stack. See the original
|
||||
# abliteration paper (https://arxiv.org/abs/2406.11717) for a deeper analysis.
|
||||
#
|
||||
# Note that we always sample this parameter even though we only need it for
|
||||
# the "global" direction scope. The reason is that multivariate TPE doesn't
|
||||
# work with conditional or variable-range parameters.
|
||||
direction_index = trial.suggest_float(
|
||||
"direction_index",
|
||||
0.4 * last_layer_index,
|
||||
0.9 * last_layer_index,
|
||||
)
|
||||
|
||||
if direction_scope == "per layer":
|
||||
direction_index = None
|
||||
|
||||
weight_distributions = {}
|
||||
|
||||
for component in model.get_abliterable_components():
|
||||
# The parameter ranges are based on experiments with various models
|
||||
# and much wider ranges. They are not set in stone and might have to be
|
||||
# adjusted for future models.
|
||||
#
|
||||
# The MLP gets a negative lower bound that is then clamped to 0, so the
|
||||
# optimizer can fully disable its ablation. The clamp puts a positive
|
||||
# probability mass on exactly 0 (the continuous sampler would otherwise
|
||||
# reach 0 with probability zero). Ablating the MLP is often unnecessary for
|
||||
# removing refusals and tends to damage model intelligence more than
|
||||
# ablating the attention output, so on many models the optimum is to leave
|
||||
# it (mostly) untouched. See issue #202.
|
||||
max_weight_lower_bound = -0.25 if component == "mlp.down_proj" else 0.8
|
||||
max_weight = max(
|
||||
0.0,
|
||||
trial.suggest_float(
|
||||
f"{component}.max_weight",
|
||||
max_weight_lower_bound,
|
||||
1.5,
|
||||
),
|
||||
)
|
||||
max_weight_position = trial.suggest_float(
|
||||
f"{component}.max_weight_position",
|
||||
0.6 * last_layer_index,
|
||||
1.0 * last_layer_index,
|
||||
)
|
||||
# For sampling purposes, min_weight is expressed as a fraction of max_weight,
|
||||
# again because multivariate TPE doesn't support variable-range parameters.
|
||||
# The value is transformed into the actual min_weight value below.
|
||||
min_weight = trial.suggest_float(
|
||||
f"{component}.min_weight",
|
||||
0.0,
|
||||
1.0,
|
||||
)
|
||||
min_weight_distance = trial.suggest_float(
|
||||
f"{component}.min_weight_distance",
|
||||
1.0,
|
||||
max(0.6 * last_layer_index, 1.0),
|
||||
)
|
||||
|
||||
weight_distributions[component] = WeightDistribution(
|
||||
max_weight=max_weight,
|
||||
max_weight_position=max_weight_position,
|
||||
min_weight=(min_weight * max_weight),
|
||||
min_weight_distance=min_weight_distance,
|
||||
)
|
||||
|
||||
return Parameters(
|
||||
direction_index=direction_index,
|
||||
weight_distributions=weight_distributions,
|
||||
)
|
||||
|
||||
def modify_model(self, ctx: Context, parameters: Parameters) -> None:
|
||||
model = ctx.get_model()
|
||||
|
||||
if parameters.direction_index is None:
|
||||
residual_direction = None
|
||||
else:
|
||||
# The index must be shifted by 1 because the first element
|
||||
# of residual_directions is the direction for the embeddings.
|
||||
weight, index = math.modf(parameters.direction_index + 1)
|
||||
residual_direction = F.normalize(
|
||||
self.residual_directions[int(index)].lerp(
|
||||
self.residual_directions[int(index) + 1],
|
||||
weight,
|
||||
),
|
||||
p=2,
|
||||
dim=0,
|
||||
)
|
||||
|
||||
# Note that some implementations of abliteration also orthogonalize
|
||||
# the embedding matrix, but it's unclear if that has any benefits.
|
||||
for layer_index in range(len(model.get_layers())):
|
||||
for component, modules in model.get_layer_modules(layer_index).items():
|
||||
weight_distribution = parameters.weight_distributions[component]
|
||||
|
||||
# Type inference fails here for some reason.
|
||||
distance = cast(
|
||||
float, abs(layer_index - weight_distribution.max_weight_position)
|
||||
)
|
||||
|
||||
# Don't orthogonalize layers that are more than
|
||||
# min_weight_distance away from max_weight_position.
|
||||
if distance > weight_distribution.min_weight_distance:
|
||||
continue
|
||||
|
||||
# Interpolate linearly between max_weight and min_weight
|
||||
# over min_weight_distance.
|
||||
weight = weight_distribution.max_weight + (
|
||||
distance / weight_distribution.min_weight_distance
|
||||
) * (weight_distribution.min_weight - weight_distribution.max_weight)
|
||||
|
||||
# A weight of 0 disables this component's ablation. reset_model() has
|
||||
# already left the adapter at identity, so abort before the otherwise
|
||||
# wasteful decomposition (which would also be operating on a zero matrix).
|
||||
if weight == 0:
|
||||
continue
|
||||
|
||||
if residual_direction is None:
|
||||
# The index must be shifted by 1 because the first element
|
||||
# of residual_directions is the direction for the embeddings.
|
||||
layer_residual_direction = self.residual_directions[layer_index + 1]
|
||||
else:
|
||||
layer_residual_direction = residual_direction
|
||||
|
||||
for module in modules:
|
||||
# FIXME: This cast is potentially invalid, because the program logic
|
||||
# does not guarantee that the module is of type Linear, and in fact
|
||||
# the retrieved modules might not conform to the interface assumed
|
||||
# below (though they do in practice). However, this is difficult
|
||||
# to fix cleanly, because get_layer_modules is called twice on
|
||||
# different model configurations, and PEFT employs different
|
||||
# module types depending on the chosen quantization.
|
||||
module = cast(Linear, module)
|
||||
|
||||
# LoRA abliteration: delta W = -lambda * v * (v^T W)
|
||||
# lora_B = -lambda * v
|
||||
# lora_A = v^T W
|
||||
|
||||
# Use the FP32 residual direction directly (no downcast/upcast)
|
||||
# and move to the correct device.
|
||||
v = layer_residual_direction.to(module.weight.device)
|
||||
|
||||
# Get W (dequantize if necessary).
|
||||
#
|
||||
# FIXME: This cast is valid only under the assumption that the original
|
||||
# module wrapped by the LoRA adapter has a weight attribute.
|
||||
# See the comment above for why this is currently not guaranteed.
|
||||
base_weight = cast(Tensor, module.base_layer.weight)
|
||||
quant_state = getattr(base_weight, "quant_state", None)
|
||||
|
||||
if quant_state is None:
|
||||
W = base_weight.to(torch.float32)
|
||||
else:
|
||||
# 4-bit quantization.
|
||||
# This cast is always valid. Type inference fails here because the
|
||||
# bnb.functional module is not found by ty for some reason.
|
||||
W = cast(
|
||||
Tensor,
|
||||
bnb.functional.dequantize_4bit( # ty:ignore[possibly-missing-attribute]
|
||||
base_weight.data,
|
||||
quant_state,
|
||||
).to(torch.float32),
|
||||
)
|
||||
|
||||
# Flatten weight matrix to (out_features, in_features).
|
||||
W = W.view(W.shape[0], -1)
|
||||
|
||||
if self.settings.row_normalization == RowNormalization.FULL:
|
||||
# Keep a reference to the original weight matrix so we can subtract it later.
|
||||
W_org = W
|
||||
|
||||
if self.settings.row_normalization != RowNormalization.NONE:
|
||||
# Get the row norms.
|
||||
W_row_norms = LA.vector_norm(W, dim=1, keepdim=True)
|
||||
# Normalize the weight matrix along the rows.
|
||||
W = F.normalize(W, p=2, dim=1)
|
||||
|
||||
# Calculate lora_A = v^T W
|
||||
# v is (d_out,), W is (d_out, d_in)
|
||||
# v @ W -> (d_in,)
|
||||
lora_A = (v @ W).view(1, -1)
|
||||
|
||||
# Calculate lora_B = -weight * v
|
||||
# v is (d_out,)
|
||||
lora_B = (-weight * v).view(-1, 1)
|
||||
|
||||
if self.settings.row_normalization == RowNormalization.PRE:
|
||||
# Make the LoRA adapter apply to the original weight matrix.
|
||||
lora_B = W_row_norms * lora_B
|
||||
elif self.settings.row_normalization == RowNormalization.FULL:
|
||||
# Approximates https://huggingface.co/blog/grimjim/norm-preserving-biprojected-abliteration
|
||||
W = W + lora_B @ lora_A
|
||||
# Normalize the adjusted weight matrix along the rows.
|
||||
W = F.normalize(W, p=2, dim=1)
|
||||
# Restore the original row norms of the weight matrix.
|
||||
W = W * W_row_norms
|
||||
# Subtract the original matrix to turn W into a delta.
|
||||
W = W - W_org
|
||||
# Use a low-rank SVD to get an approximation of the matrix.
|
||||
r = model.peft_config.r
|
||||
|
||||
# svd_lowrank is randomized:
|
||||
# https://github.com/pytorch/pytorch/blob/20919052303c0b5ba87f8bf7e19237dc33ab09d3/torch/_lowrank.py#L108-L109
|
||||
# Reseed immediately before the call so restoring a trial is independent of RNG history.
|
||||
torch.manual_seed(self.heretic_settings.seed)
|
||||
# "It's safe to call this function if CUDA is not available;
|
||||
# in that case, it is silently ignored."
|
||||
torch.cuda.manual_seed_all(self.heretic_settings.seed) # ty:ignore[invalid-argument-type]
|
||||
U, S, Vh = torch.svd_lowrank(W, q=2 * r + 4, niter=6)
|
||||
|
||||
# Truncate it to the part we want to store in the LoRA adapter.
|
||||
# Note: svd_lowrank actually returns V, so transpose it to get Vh.
|
||||
U = U[:, :r]
|
||||
S = S[:r]
|
||||
Vh = Vh[:, :r].T
|
||||
# Transfer it into the LoRA adapter components. Split the singular values
|
||||
# evenly between the two components to keep their norms balanced and avoid
|
||||
# potential issues with numerical stability.
|
||||
sqrt_S = torch.sqrt(S)
|
||||
lora_B = U @ torch.diag(sqrt_S)
|
||||
lora_A = torch.diag(sqrt_S) @ Vh
|
||||
|
||||
# Assign to adapters. The adapter name is "default", because that's
|
||||
# what PEFT uses when no name is explicitly specified, as above.
|
||||
# These casts are therefore valid.
|
||||
weight_A = cast(Tensor, module.lora_A["default"].weight)
|
||||
weight_B = cast(Tensor, module.lora_B["default"].weight)
|
||||
weight_A.data = lora_A.to(weight_A.dtype)
|
||||
weight_B.data = lora_B.to(weight_B.dtype)
|
||||
|
||||
def reset_model(self, ctx: Context) -> None:
|
||||
model = ctx.get_model()
|
||||
fast_path = model.reset_model()
|
||||
if not fast_path:
|
||||
model.apply_lora(self.lora_rank)
|
||||
+55
-5
@@ -13,17 +13,17 @@ from typing import Annotated, Any, TypeVar, Union, get_args, get_origin, get_typ
|
||||
from pydantic import BaseModel
|
||||
from torch import Tensor
|
||||
|
||||
from heretic.utils import Prompt, load_prompts
|
||||
|
||||
from .config import DatasetSpecification
|
||||
from .config import Settings as HereticSettings
|
||||
from .model import Model
|
||||
from .utils import Prompt, deep_merge_dicts, load_prompts
|
||||
|
||||
T = TypeVar("T")
|
||||
|
||||
|
||||
def get_plugin_namespace(
|
||||
model_extra: dict[str, Any] | None, namespace: str
|
||||
model_extra: dict[str, Any] | None,
|
||||
namespace: str,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Returns the config dict from the `[<namespace>]` TOML table.
|
||||
@@ -51,7 +51,7 @@ def is_builtin_plugin(name: str) -> bool:
|
||||
plugins (file paths or third-party import paths) disable the reproducibility
|
||||
offer during upload.
|
||||
"""
|
||||
return name.startswith("heretic.scorers.")
|
||||
return name.startswith("heretic.")
|
||||
|
||||
|
||||
def load_plugin(
|
||||
@@ -286,9 +286,46 @@ class Plugin:
|
||||
)
|
||||
return model
|
||||
|
||||
@classmethod
|
||||
def get_settings_raw(
|
||||
cls,
|
||||
model_extra: dict[str, Any] | None,
|
||||
top_namespace: str,
|
||||
instance_name: str | None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Build the raw settings dict for a plugin class and optional instance.
|
||||
|
||||
Config rules:
|
||||
- Base settings live in `[<top_namespace>.ClassName]` (applies to all instances).
|
||||
- Instance overrides live in `[<top_namespace>.ClassName_<instance_name>]` (preferred).
|
||||
- Only merge/validate keys that exist in the plugin Settings schema.
|
||||
"""
|
||||
settings_model = cls.get_settings_model()
|
||||
if settings_model is None:
|
||||
# No settings schema: nothing to merge/validate.
|
||||
return {}
|
||||
|
||||
class_name = cls.__name__
|
||||
|
||||
namespaces = [f"{top_namespace}.{class_name}"]
|
||||
if instance_name:
|
||||
namespaces.append(f"{top_namespace}.{class_name}_{instance_name}")
|
||||
|
||||
merged_settings: dict[str, Any] = {}
|
||||
allowed_keys = set(settings_model.model_fields.keys())
|
||||
|
||||
for namespace in namespaces:
|
||||
raw_table = get_plugin_namespace(model_extra, namespace)
|
||||
filtered = {k: v for k, v in raw_table.items() if k in allowed_keys}
|
||||
merged_settings = deep_merge_dicts(merged_settings, filtered)
|
||||
|
||||
return merged_settings
|
||||
|
||||
@classmethod
|
||||
def validate_settings(
|
||||
cls, raw_namespace: dict[str, Any] | None
|
||||
cls,
|
||||
raw_namespace: dict[str, Any] | None,
|
||||
) -> BaseModel | None:
|
||||
"""
|
||||
Validates plugin settings for this plugin class.
|
||||
@@ -301,6 +338,19 @@ class Plugin:
|
||||
return None
|
||||
return settings_model.model_validate(raw_namespace or {})
|
||||
|
||||
def get_dataset_specifications(self) -> list[DatasetSpecification]:
|
||||
"""
|
||||
Collect the dataset specifications declared in the settings
|
||||
of the plugin.
|
||||
"""
|
||||
if self.settings is None:
|
||||
return []
|
||||
specifications = []
|
||||
for value in dict(self.settings).values():
|
||||
if isinstance(value, DatasetSpecification):
|
||||
specifications.append(value)
|
||||
return specifications
|
||||
|
||||
def init(self, ctx: Context) -> None:
|
||||
"""
|
||||
Runs before the plugin's main functionality.
|
||||
|
||||
@@ -6,9 +6,8 @@ from dataclasses import dataclass
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from heretic.plugin import Context, Plugin
|
||||
|
||||
from .config import Settings as HereticSettings
|
||||
from .plugin import Context, Plugin
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
@@ -50,11 +50,6 @@ class Settings(BaseModel):
|
||||
description="Name that describes what the configured keyword rate measures.",
|
||||
)
|
||||
|
||||
keyword_markers: list[str] = Field(
|
||||
default=REFUSAL_MARKERS,
|
||||
description="Strings whose presence in a response (case insensitive) identifies the response as a keyword match.",
|
||||
)
|
||||
|
||||
prompts: DatasetSpecification = Field(
|
||||
default=DatasetSpecification(
|
||||
dataset="mlabonne/harmful_behaviors",
|
||||
@@ -64,6 +59,11 @@ class Settings(BaseModel):
|
||||
description="Dataset of prompts to evaluate the keyword match rate on.",
|
||||
)
|
||||
|
||||
keyword_markers: list[str] = Field(
|
||||
default=REFUSAL_MARKERS,
|
||||
description="Strings whose presence in a response (case insensitive) identifies the response as a keyword match.",
|
||||
)
|
||||
|
||||
print_responses: bool = Field(
|
||||
default=False,
|
||||
description="Whether to print prompt/response pairs when counting keyword matches.",
|
||||
|
||||
@@ -17,7 +17,7 @@ class Settings(BaseModel):
|
||||
split="test[:100]",
|
||||
column="text",
|
||||
),
|
||||
description="Prompt dataset used to measure KL divergence from original model.",
|
||||
description="Dataset of prompts used to measure KL divergence from original model.",
|
||||
)
|
||||
|
||||
|
||||
|
||||
+14
-26
@@ -1,6 +1,8 @@
|
||||
# SPDX-License-Identifier: AGPL-3.0-or-later
|
||||
# Copyright (C) 2025-2026 Philipp Emanuel Weidmann <pew@worldwidemann.com> + contributors
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import json
|
||||
import os
|
||||
@@ -11,7 +13,7 @@ from dataclasses import dataclass
|
||||
from datetime import datetime, timezone
|
||||
from importlib.metadata import version
|
||||
from pathlib import Path
|
||||
from typing import Any, TypeVar
|
||||
from typing import TYPE_CHECKING, Any, TypeVar
|
||||
|
||||
import huggingface_hub
|
||||
import tomli_w
|
||||
@@ -38,6 +40,10 @@ from .system import (
|
||||
is_xpu_available,
|
||||
)
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .modifier import Modifier
|
||||
|
||||
|
||||
T = TypeVar("T")
|
||||
|
||||
|
||||
@@ -257,23 +263,9 @@ def batchify(items: list[T], batch_size: int) -> list[list[T]]:
|
||||
return [items[i : i + batch_size] for i in range(0, len(items), batch_size)]
|
||||
|
||||
|
||||
def get_trial_parameters(trial: Trial | FrozenTrial) -> dict[str, str]:
|
||||
params = {}
|
||||
|
||||
direction_index = trial.user_attrs["direction_index"]
|
||||
params["direction_index"] = (
|
||||
"per layer" if (direction_index is None) else f"{direction_index:.2f}"
|
||||
)
|
||||
|
||||
for component, parameters in trial.user_attrs["parameters"].items():
|
||||
for name, value in parameters.items():
|
||||
params[f"{component}.{name}"] = f"{value:.2f}"
|
||||
|
||||
return params
|
||||
|
||||
|
||||
def get_readme_intro(
|
||||
settings: Settings,
|
||||
modifier: Modifier[Any],
|
||||
trial: Trial | FrozenTrial,
|
||||
contains_reproducibility_information: bool,
|
||||
) -> str:
|
||||
@@ -316,7 +308,7 @@ def get_readme_intro(
|
||||
model_link
|
||||
}, made using [Heretic](https://heretic-project.org) v{version("heretic-llm")}
|
||||
{reproducibility_instructions}
|
||||
## Abliteration parameters
|
||||
## {modifier.modifier_name} parameters
|
||||
|
||||
| Parameter | Value |
|
||||
| :-------- | :---: |
|
||||
@@ -324,7 +316,7 @@ def get_readme_intro(
|
||||
chr(10).join(
|
||||
[
|
||||
f"| **{name}** | {value} |"
|
||||
for name, value in get_trial_parameters(trial).items()
|
||||
for name, value in modifier.render_trial_parameters(trial).items()
|
||||
]
|
||||
)
|
||||
}
|
||||
@@ -508,8 +500,7 @@ This directory contains the necessary information and assets to reproduce the re
|
||||
|
||||
## Datasets
|
||||
|
||||
- **Good prompts:** {format_hf_link(settings.good_prompts.dataset, settings.good_prompts.commit, is_dataset=True)}
|
||||
- **Bad prompts:** {format_hf_link(settings.bad_prompts.dataset, settings.bad_prompts.commit, is_dataset=True)}
|
||||
- TODO: Collect all datasets from scorers and modifiers.
|
||||
|
||||
## Selected trial
|
||||
|
||||
@@ -564,8 +555,8 @@ def generate_reproduce_json(
|
||||
version_info = get_heretic_version_info()
|
||||
|
||||
data = {
|
||||
# Version 3: plugin-based schema with generic scores/baseline scores.
|
||||
"version": "3",
|
||||
# Version 4: plugin-based schema with generic parameters and scores.
|
||||
"version": "4",
|
||||
"timestamp": timestamp,
|
||||
"system": None, # Defined here to preserve insertion order.
|
||||
"environment": {
|
||||
@@ -578,10 +569,7 @@ def generate_reproduce_json(
|
||||
"requirements": get_requirements_dict(),
|
||||
},
|
||||
"settings": settings.model_dump(),
|
||||
"parameters": {
|
||||
"direction_index": trial.user_attrs["direction_index"],
|
||||
"abliteration_parameters": trial.user_attrs["parameters"],
|
||||
},
|
||||
"parameters": trial.user_attrs["parameters"],
|
||||
"scores": trial.user_attrs["scores"],
|
||||
"hashes": uploaded_model_hashes,
|
||||
}
|
||||
|
||||
+13
-13
@@ -1,4 +1,4 @@
|
||||
# This test case is for Hybrid-Edge models.
|
||||
# This test case is for hybrid models.
|
||||
# After any change related to it, this test should PASS.
|
||||
|
||||
model = "tiny-random/gemma-4e"
|
||||
@@ -18,18 +18,6 @@ trial_index = 0
|
||||
model_action = "save"
|
||||
save_directory = "model"
|
||||
|
||||
[good_prompts]
|
||||
dataset = "mlabonne/harmless_alpaca"
|
||||
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
||||
split = "train[:5]"
|
||||
column = "text"
|
||||
|
||||
[bad_prompts]
|
||||
dataset = "mlabonne/harmful_behaviors"
|
||||
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
||||
split = "train[:5]"
|
||||
column = "text"
|
||||
|
||||
[scorer.KLDivergence.prompts]
|
||||
dataset = "mlabonne/harmless_alpaca"
|
||||
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
||||
@@ -41,3 +29,15 @@ dataset = "mlabonne/harmful_behaviors"
|
||||
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
||||
split = "test[:5]"
|
||||
column = "text"
|
||||
|
||||
[modifier.Abliteration.good_prompts]
|
||||
dataset = "mlabonne/harmless_alpaca"
|
||||
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
||||
split = "train[:5]"
|
||||
column = "text"
|
||||
|
||||
[modifier.Abliteration.bad_prompts]
|
||||
dataset = "mlabonne/harmful_behaviors"
|
||||
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
||||
split = "train[:5]"
|
||||
column = "text"
|
||||
|
||||
+15
-14
@@ -18,20 +18,6 @@ trial_index = 0
|
||||
model_action = "save"
|
||||
save_directory = "model"
|
||||
|
||||
row_normalization = "none"
|
||||
|
||||
[good_prompts]
|
||||
dataset = "mlabonne/harmless_alpaca"
|
||||
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
||||
split = "train[:5]"
|
||||
column = "text"
|
||||
|
||||
[bad_prompts]
|
||||
dataset = "mlabonne/harmful_behaviors"
|
||||
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
||||
split = "train[:5]"
|
||||
column = "text"
|
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|
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[scorer.KLDivergence.prompts]
|
||||
dataset = "mlabonne/harmless_alpaca"
|
||||
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
||||
@@ -43,3 +29,18 @@ dataset = "mlabonne/harmful_behaviors"
|
||||
commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
||||
split = "test[:5]"
|
||||
column = "text"
|
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|
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[modifier.Abliteration]
|
||||
row_normalization = "none"
|
||||
|
||||
[modifier.Abliteration.good_prompts]
|
||||
dataset = "mlabonne/harmless_alpaca"
|
||||
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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split = "train[:5]"
|
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column = "text"
|
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|
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[modifier.Abliteration.bad_prompts]
|
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dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
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split = "train[:5]"
|
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column = "text"
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+13
-13
@@ -1,4 +1,4 @@
|
||||
# This test case is for Dense models.
|
||||
# This test case is for dense models.
|
||||
# After any change related to it, this test should PASS.
|
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|
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model = "tiny-random/mistral-3"
|
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@@ -18,18 +18,6 @@ trial_index = 0
|
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model_action = "save"
|
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save_directory = "model"
|
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|
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[good_prompts]
|
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dataset = "mlabonne/harmless_alpaca"
|
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commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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split = "train[:5]"
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column = "text"
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[bad_prompts]
|
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dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
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split = "train[:5]"
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column = "text"
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[scorer.KLDivergence.prompts]
|
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dataset = "mlabonne/harmless_alpaca"
|
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commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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@@ -41,3 +29,15 @@ dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
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split = "test[:5]"
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column = "text"
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[modifier.Abliteration.good_prompts]
|
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dataset = "mlabonne/harmless_alpaca"
|
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commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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split = "train[:5]"
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column = "text"
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|
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[modifier.Abliteration.bad_prompts]
|
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dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
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split = "train[:5]"
|
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column = "text"
|
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|
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+15
-14
@@ -18,20 +18,6 @@ trial_index = 0
|
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model_action = "save"
|
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save_directory = "model"
|
||||
|
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row_normalization = "pre"
|
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|
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[good_prompts]
|
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dataset = "mlabonne/harmless_alpaca"
|
||||
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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split = "train[:5]"
|
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column = "text"
|
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|
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[bad_prompts]
|
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dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
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split = "train[:5]"
|
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column = "text"
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|
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[scorer.KLDivergence.prompts]
|
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dataset = "mlabonne/harmless_alpaca"
|
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commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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@@ -43,3 +29,18 @@ dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
||||
split = "test[:5]"
|
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column = "text"
|
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|
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[modifier.Abliteration]
|
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row_normalization = "pre"
|
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|
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[modifier.Abliteration.good_prompts]
|
||||
dataset = "mlabonne/harmless_alpaca"
|
||||
commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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split = "train[:5]"
|
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column = "text"
|
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|
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[modifier.Abliteration.bad_prompts]
|
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dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
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split = "train[:5]"
|
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column = "text"
|
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|
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@@ -18,18 +18,6 @@ trial_index = 0
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model_action = "save"
|
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save_directory = "model"
|
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|
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[good_prompts]
|
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dataset = "mlabonne/harmless_alpaca"
|
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commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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split = "train[:5]"
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column = "text"
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[bad_prompts]
|
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dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
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split = "train[:5]"
|
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column = "text"
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[scorer.KLDivergence.prompts]
|
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dataset = "mlabonne/harmless_alpaca"
|
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commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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@@ -41,3 +29,15 @@ dataset = "mlabonne/harmful_behaviors"
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
|
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split = "test[:5]"
|
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column = "text"
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[modifier.Abliteration.good_prompts]
|
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dataset = "mlabonne/harmless_alpaca"
|
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commit = "02c6a92cfcf11bb0c387334f8146d149d65b587f"
|
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split = "train[:5]"
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column = "text"
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[modifier.Abliteration.bad_prompts]
|
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commit = "01cead01398926d81f7c52bdb790ee8cf77ebba7"
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split = "train[:5]"
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@@ -231,15 +231,6 @@ wheels = [
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[[package]]
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[[package]]
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@@ -429,162 +420,6 @@ wheels = [
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Reference in New Issue
Block a user