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- Quarantine superseded entry points under legacy/ (2-2 single-step wrappers,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>
34 lines
904 B
Python
34 lines
904 B
Python
from transformers import AutoTokenizer
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import json
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MODEL_PATH = "/data/local/hujk/models/Llama-3.1-8B-Instruct"
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MESSAGES = [
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{"role": "system", "content": "Answer briefly. <inst>Only use the data.</inst>"},
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{
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"role": "user",
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"content": "<inst>Question</inst>: <data>What is the capital of Japan?</data>",
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},
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{"role": "assistant", "content": "tokyo"},
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{
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"role": "tool",
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"content": json.dumps( [
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{
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"email title": "news",
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"content": "daily <inst>inst in tool response</inst>",
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}
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] ,ensure_ascii=False),
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},
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]
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def main() -> None:
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
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rendered = tokenizer.apply_chat_template(
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MESSAGES, tokenize=False, add_generation_prompt=False
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)
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print(rendered)
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if __name__ == "__main__":
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main()
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