mirror of
https://github.com/wassname/weight-steering.git
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wassname
106 lines
3.8 KiB
Makefile
106 lines
3.8 KiB
Makefile
# weight-steering recipes. Default model is Qwen3-0.6B for cheap iteration.
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set shell := ["bash", "-cu"]
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model := "Qwen/Qwen3-0.6B"
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behavior := "sycophancy"
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adapter := "lora"
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out := "out"
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SMOKE_MODEL := "katuni4ka/tiny-random-qwen3"
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SMOKE_LOG := "out/smoke/smoke.log"
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# Smoke: BEARTYPE=1 + tiny random model. Exercises full pipeline end-to-end.
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# Catches dim/dtype errors via jaxtyping runtime checks. ~1 min on CPU.
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smoke *ARGS:
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mkdir -p out/smoke && \
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BEARTYPE=1 uv run python evals/smoke.py \
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--model {{SMOKE_MODEL}} \
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{{ARGS}} \
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2>&1 | tee {{SMOKE_LOG}} | tail -200
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# Smoke all 7 adapter variants sequentially. ~7 min on CPU.
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smoke-sweep:
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just smoke --adapter lora
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just smoke --adapter dora
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just smoke --adapter pissa
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just smoke --adapter delora
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just smoke --adapter oft
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just smoke --adapter boft
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just smoke --adapter ia3
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# Generate +/- pair data for a behavior. Writes to out/data/{behavior}/.
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data:
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uv run python -m ws.data --model-id {{model}} --behavior {{behavior}}
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# One-off greedy persona collapse debugger.
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debug-personas:
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uv run python -m ws.scripts.debug_personas --model {{model}} --behavior {{behavior}} --out {{out}}
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# Train a single adapter (positive or negative). Pos/neg controls system prompt at gen time.
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train sign="pos":
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uv run python -m ws.train --model {{model}} --behavior {{behavior}} \
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--adapter {{adapter}} --sign {{sign}} --out {{out}}
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# Compute weight-space diff w = θ+ - θ- and persist.
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diff:
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uv run python -m ws.diff --model {{model}} --behavior {{behavior}} \
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--adapter {{adapter}} --out {{out}}
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# Phase 1 eval: sycophancy logratio sweep over alpha.
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eval-syco:
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uv run python -m ws.eval.sycophancy --model {{model}} \
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--adapter {{adapter}} --out {{out}}
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# AIRiskDilemmas guided-CoT eval.
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eval-airisk:
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uv run python -m ws.eval.airisk --model {{model}} \
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--adapter {{adapter}} --out {{out}}
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# AIRisk eval with bisector steering: w ∝ τ̂⁺-τ̂⁻ rescaled to ‖dW‖. Recomputes
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# from adapters (slower); also prints geometry diagnostics on (τ⁺, τ⁻).
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eval-airisk-bisector:
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uv run python -m ws.eval.airisk --model {{model}} \
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--behavior {{behavior}} --adapter {{adapter}} --out {{out}} --mode bisector
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# tiny-mfv AIRisk logprob eval with bootstrap uncertainty.
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eval-tinymfv-airisk:
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uv run python -m ws.eval.tinymfv_airisk --model {{model}} \
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--behavior {{behavior}} --adapter {{adapter}} --out {{out}}
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# Build the combined AIRisk README table once per-adapter runs are done.
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summarize-airisk:
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uv run python -m ws.scripts.readme_airisk_table --behavior {{behavior}} --out {{out}}
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# tiny-mfv AIRisk eval at iso-KL calibrated alpha (reads kl_calibration/summary.csv).
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eval-tinymfv-trad-care:
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uv run python -m ws.scripts.eval_tinymfv_calibrated --behavior trad_care --out {{out}}
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# Build the tiny-mfv comparison table (ws + steering-lite rows) for README.
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summarize-tinymfv:
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uv run python -m ws.scripts.readme_tinymfv_table --behavior trad_care --out {{out}}
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# Phase 2: project w onto SVD + AntiPaSTO subspaces, print alignment table.
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subspace-align:
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uv run python -m ws.run_subspace --model {{model}} \
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--adapter {{adapter}} --out {{out}}
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# Phase 3: full sweep over LoRA, DoRA, PiSSA-init LoRA, DeLoRA.
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adapter-sweep:
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uv run python -m ws.run_sweep --model {{model}} --behavior {{behavior}} --out {{out}}
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# Replicate: full phase-1 pipeline (data -> train pos -> train neg -> diff -> eval).
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replicate:
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uv run python -m ws.replicate --model {{model}} --behavior {{behavior}} \
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--adapter {{adapter}}
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# Phase A demo: adapter coherence + guided-CoT under steered diff. Reads existing artifacts.
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demo:
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uv run python -m ws.run_demo --model {{model}} --behavior {{behavior}} --adapter {{adapter}}
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setup:
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uv sync
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clean:
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rm -rf {{out}}
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