0f90602339
Pipeline (stages 1 -> 4-1) can now be run in order from each stage folder.
Stage scripts:
- 2-1: make SEP/FocalLora prep portable (derive paths from __file__ instead of
hardcoded /home/hujk/...) and add prepare_head_ident_dataset.sh runner.
Verified the SEP converter reproduces the committed jsonl byte-for-byte.
- 2-2: unify the four Ident_IH_ALL_1-4_<model>.sh scripts (modernise llama to
conda hook + $ROOT/models; add the missing FocalLora step to qwen3-4b/8b so
focallora.json gets generated for them too).
- 2-3: default TARGETS now covers the three curves from the README
(all_roc_inst_0.1, user_roc_inst_0.1, focallora).
- 3-2: add combos/ with 24 scripts (4 models x {pbs,nts,nts_wam} x {squad,tri}),
head ranking pinned to all_roc_inst_0.1, TOPK overridable.
- 4-1: add eval_single.sh driver + combos/ with 24 cross-eval wrappers
(squad-trained -> tri-eval and vice versa), reusing the --eval-only path.
Eval semantics:
- Judge ASR before UTIL: a response carrying the injected answer now counts as
attacked even when it also contains the correct answer. This changes the
metric, so old training_log.csv rows are not comparable.
- Add --dev-holdout: reserve the last N source rows as a dev slice; training
drops them and the in-training quick eval uses only them. Previously the
quick eval silently defaulted to the squad evaluation set, which contradicted
the README and self-contaminated squad-trained runs.
- train_attn_kl_clean.sh now passes --eval-data-path/--eval-topicattack-path.
- Add --eval-step0 to log an untuned-baseline row before any weight update.
Housekeeping:
- Quarantine superseded entry points under legacy/ (2-2 single-step wrappers,
3-2 old _tuning.fix.* wrappers, 3-1 auxiliary), each with a README.
- Fix .gitignore: the model_score rule was anchored at the repo root and never
matched Codes/..., so ~26GB of intermediates had been staged. Now excludes
*.pkl (~25GB), heads_sorted_eval/ (~690MB), outputs_lora/ checkpoints
(~3.2GB) and pycache. heads_sorted/ and head_scoring_combined.json are kept
deliberately: they are small and are the HEAD_PATH inputs stage 3-2 needs.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
39 lines
1.3 KiB
Bash
39 lines
1.3 KiB
Bash
#!/usr/bin/env sh
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set -eu
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export CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-0}
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CONDA_BIN=${CONDA_BIN:-}
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if [ -z "$CONDA_BIN" ]; then
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if command -v conda >/dev/null 2>&1; then
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CONDA_BIN=$(command -v conda)
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elif [ -x /opt/miniconda/bin/conda ]; then
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CONDA_BIN=/opt/miniconda/bin/conda
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fi
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fi
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if [ -n "$CONDA_BIN" ]; then
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eval "$("$CONDA_BIN" shell.bash hook)"
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conda activate focallora4
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fi
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SCRIPT_DIR=$(CDPATH= cd -- "$(dirname -- "$0")" && pwd)
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BASE=$(CDPATH= cd -- "$SCRIPT_DIR/.." && pwd)
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MODEL_PATH=${MODEL_PATH:-/data/local/hujk/models/Llama-3.1-8B-Instruct}
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DATA_PATH=${DATA_PATH:-$BASE/3-1_model_training_data_gen/single_turn/tri_native_tool_response_only.json}
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HEAD_PATH=${HEAD_PATH:-$BASE/2-2_head_identification_scoring/model_score/sep_Llama-3.1-8B-Instruct/heads_sorted/all_roc_inst_0.1.json}
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OUTPUT_DIR=${OUTPUT_DIR:-$SCRIPT_DIR/outputs_lora/llama31-8b_tri_native_tool_response_only}
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python "$SCRIPT_DIR/_tuning.fix.modified.py" \
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--model_path "$MODEL_PATH" \
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--data_path "$DATA_PATH" \
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--head_path "$HEAD_PATH" \
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--output_dir "$OUTPUT_DIR" \
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--topk "${TOPK:-100p}" \
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--epochs "${EPOCHS:-30}" \
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--batch_size "${BATCH_SIZE:-6}" \
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--batch-save-interval "${BATCH_SAVE_INTERVAL:-100}" \
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--lr "${LR:-5e-4}" \
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--lambda_preserve "${LAMBDA_PRESERVE:-1.0}"
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