mirror of
https://github.com/wassname/weight-steering.git
synced 2026-07-13 17:46:39 +08:00
wassname
7be1487d7b
Subagent review fixes: - DataCfg / Cfg expose the grid directly (n_topics, n_personas, n_samples) as required ints with paper defaults (20/5/10). Drops `n_pairs` and the silent round() that made the count fuzzy. Drops `Optional[int]` smoke overrides — smoke just sets 2/1/2 = 4 pairs. - Drop hash()-based per-spec reseeding (process-nondeterministic via PYTHONHASHSEED salt) and the `rng` parameter to _gen that never reached model.generate. One torch.manual_seed at start; spec order seeded by rng. - Delete _judge_filter stub + cfg.judge flag (dead code, paper §3 GPT-4.1-mini filter not implemented yet — TODO comment instead). - replicate._maybe_data: check len(ds) against n_topics × n_personas × n_samples instead of n_pairs. - justfile: drop --n-pairs 1000.
69 lines
2.3 KiB
Makefile
69 lines
2.3 KiB
Makefile
# weight-steering recipes. Default model is Qwen3-0.6B for cheap iteration.
|
|
|
|
set shell := ["bash", "-cu"]
|
|
|
|
model := "Qwen/Qwen3-0.6B"
|
|
behavior := "sycophancy"
|
|
adapter := "lora"
|
|
out := "out"
|
|
|
|
SMOKE_MODEL := "katuni4ka/tiny-random-qwen3"
|
|
SMOKE_LOG := "out/smoke/smoke.log"
|
|
|
|
# Smoke: BEARTYPE=1 + tiny random model. Exercises full pipeline end-to-end.
|
|
# Catches dim/dtype errors via jaxtyping runtime checks. ~1 min on CPU.
|
|
smoke *ARGS:
|
|
mkdir -p out/smoke && \
|
|
BEARTYPE=1 uv run python evals/smoke.py \
|
|
--model {{SMOKE_MODEL}} \
|
|
{{ARGS}} \
|
|
2>&1 | tee {{SMOKE_LOG}} | tail -200
|
|
|
|
# Generate +/- pair data for a behavior. Writes to out/data/{behavior}/.
|
|
data:
|
|
uv run python -m ws.data --model {{model}} --behavior {{behavior}}
|
|
|
|
# Train a single adapter (positive or negative). Pos/neg controls system prompt at gen time.
|
|
train sign="pos":
|
|
uv run python -m ws.train --model {{model}} --behavior {{behavior}} \
|
|
--adapter {{adapter}} --sign {{sign}} --out {{out}}
|
|
|
|
# Compute weight-space diff w = θ+ - θ- and persist.
|
|
diff:
|
|
uv run python -m ws.diff --model {{model}} --behavior {{behavior}} \
|
|
--adapter {{adapter}} --out {{out}}
|
|
|
|
# Phase 1 eval: sycophancy logratio sweep over alpha.
|
|
eval-syco:
|
|
uv run python -m ws.eval.sycophancy --model {{model}} \
|
|
--adapter {{adapter}} --out {{out}}
|
|
|
|
# Phase 4 eval: daily dilemmas Yes/No logratio.
|
|
eval-dilemmas:
|
|
uv run python -m ws.eval.dilemmas --model {{model}} \
|
|
--adapter {{adapter}} --out {{out}}
|
|
|
|
# Phase 2: project w onto SVD + AntiPaSTO subspaces, print alignment table.
|
|
subspace-align:
|
|
uv run python -m ws.run_subspace --model {{model}} \
|
|
--adapter {{adapter}} --out {{out}}
|
|
|
|
# Phase 3: full sweep over LoRA, DoRA, PiSSA-init LoRA, DeLoRA.
|
|
adapter-sweep:
|
|
uv run python -m ws.run_sweep --model {{model}} --behavior {{behavior}} --out {{out}}
|
|
|
|
# Replicate: full phase-1 pipeline (data -> train pos -> train neg -> diff -> eval).
|
|
replicate:
|
|
uv run python -m ws.replicate --model {{model}} --behavior {{behavior}} \
|
|
--adapter {{adapter}}
|
|
|
|
# Phase A demo: adapter coherence + guided-CoT under steered diff. Reads existing artifacts.
|
|
demo:
|
|
uv run python -m ws.run_demo --model {{model}} --behavior {{behavior}} --adapter {{adapter}}
|
|
|
|
setup:
|
|
uv sync
|
|
|
|
clean:
|
|
rm -rf {{out}}
|