Text-DPO - #53
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📝 WalkthroughWalkthroughChangesThe PR adds an SEC refusal-calibration DPO workflow. It defines typed calibration records, generates labeled candidates, builds and judges preference pairs, supports optional CUDA LoRA training, and adds Notebook 06 documentation and evaluation. It also adds transaction dataset metadata. SEC DPO workflow
Transaction dataset metadata
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: 🟡 Moderate · up to The PR adds DPO generation and training, but the current head can produce invalid synthetic account values, abort on malformed or failed model responses, and fail by default on CPU-only notebook runs. These correctness and usability issues should be fixed or explicitly accepted before merge. Sequence Diagram(s)sequenceDiagram
participant SECTrainingData
participant DPOGeneration
participant PairBuilder
participant Judge
participant LoRATrainer
SECTrainingData->>DPOGeneration: SEC training scope-boundary paragraphs
DPOGeneration->>DPOGeneration: Generate boundary questions and four candidates
DPOGeneration->>PairBuilder: Calibration prompts
PairBuilder->>PairBuilder: Build chosen-versus-rejected pairs
PairBuilder->>Judge: Optionally validate each pair
Judge-->>PairBuilder: Pair winner
PairBuilder->>LoRATrainer: Accepted preference pairs
LoRATrainer-->>SECTrainingData: Save adapter and evaluation artifacts
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 61.11% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 36 functions across 8 files. (9 skipped: 9 unsupported.) Full details: Description checkExplanation The description summarizes the DPO notebook and submodule, identifies the new feature, lists the main changes, and reports testing and manual validation. Optional sections do not require additional information. ✨ Finishing Touches 💡 1📝 Generate docstrings 💡
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Inline comments:
In `@aieng-synthetic-data/aieng/syn_data/text/dpo/generate.py`:
- Around line 61-79: Update _complete_json to validate that both complete_json
results and json.loads(extract_json_text(raw)) produce a dictionary at runtime,
matching the guard used by _pairwise_winner; reject non-dict payloads before
callers invoke payload.get(...), while preserving the existing dict return
behavior.
In `@aieng-synthetic-data/aieng/syn_data/text/dpo/pairs.py`:
- Around line 125-135: Update the exception tuple in filter_pairs_with_judge
around the _pairwise_winner call to include requests.HTTPError, ensuring
re-raised judge HTTP failures are logged, the current pair is added to dropped,
and filtering continues.
In `@implementations/qa_text_generation/.env.example`:
- Around line 27-29: Set the RUN_DPO default in the environment example to 0 so
copying the file does not enable CUDA-only LoRA DPO; users should explicitly
change it to 1 on supported NVIDIA CUDA environments.
In `@implementations/qa_text_generation/06_dpo_preference_pairs.ipynb`:
- Around line 1068-1077: Update the evaluation setup around DPO_ADAPTER_DIR,
PeftInferenceClient, and eval_label to skip evaluation when the adapter is
unavailable instead of raising FileNotFoundError. Ensure RUN_DPO=0 does not
produce an error while preserving evaluation setup when a valid adapter exists.
In `@implementations/tabular_data/single_table/data/meta_info.json`:
- Around line 2-6: Classify account as discrete across all transaction metadata:
in implementations/tabular_data/single_table/data/meta_info.json:2-6, move
column index 7 from num_col_idx to cat_col_idx; in
implementations/tabular_data/single_table/data/trans_domain.json:30-33 and
implementations/tabular_data/single_table/data/xxtrans_domain.json:1-1, change
account.type to discrete.
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⛔ Files ignored due to path filters (7)
implementations/qa_text_generation/images/DPO.pngis excluded by!**/*.pngimplementations/tabular_data/single_table/data/raw_data/trans.csvis excluded by!**/*.csvimplementations/tabular_data/single_table/data/trans.csvis excluded by!**/*.csvimplementations/tabular_data/single_table/data/trans_holdout.csvis excluded by!**/*.csvimplementations/tabular_data/single_table/results/before_matching/synthetic_tables.pklis excluded by!**/*.pklimplementations/tabular_data/single_table/results/models/None_trans_ckpt.pklis excluded by!**/*.pklimplementations/tabular_data/single_table/results/trans_synthetic.csvis excluded by!**/*.csv
📒 Files selected for processing (17)
aieng-synthetic-data/aieng/syn_data/text/dpo/__init__.pyaieng-synthetic-data/aieng/syn_data/text/dpo/config.pyaieng-synthetic-data/aieng/syn_data/text/dpo/generate.pyaieng-synthetic-data/aieng/syn_data/text/dpo/pairs.pyaieng-synthetic-data/aieng/syn_data/text/dpo/prompts.pyaieng-synthetic-data/aieng/syn_data/text/dpo/schemas.pyaieng-synthetic-data/aieng/syn_data/text/dpo/train.pyaieng-synthetic-data/pyproject.tomlaieng-synthetic-data/tests/test_text_dpo.pyimplementations/qa_text_generation/.env.exampleimplementations/qa_text_generation/06_dpo_preference_pairs.ipynbimplementations/qa_text_generation/README.mdimplementations/tabular_data/single_table/data/dataset_meta.jsonimplementations/tabular_data/single_table/data/meta_info.jsonimplementations/tabular_data/single_table/data/trans_domain.jsonimplementations/tabular_data/single_table/data/xxtrans_domain.jsonpyproject.toml
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…into ft/test-DPO Co-authored-by: Cursor <cursoragent@cursor.com> # Conflicts: # implementations/qa_text_generation/README.md
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Summary
Added one notebook and a DPO submodule to showcase generating preference data for preference optimization. Then optionally fine-tune the baseline model with LoRA DPO and the preference data. This improves the SLM response quality for refusal calibration samples in the test set.
Clickup Ticket(s): Link(s) if applicable.
Type of Change
Changes Made
Testing
uv run pytest tests/)uv run mypy <src_dir>)uv run ruff check src_dir/)Manual testing details:
Ran the notebook on coder.
Checklist
Summary by CodeRabbit
New Features
Documentation
Tests