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Add training scripts and utilities for NodeAudit and GraphReview
- Implement `train_lora.py` for LoRA training pipeline, including dataset preparation and model training. - Create `trajectory_collector.py` to collect trajectories and DPO pairs from the CodeReview environment. - Develop `training_graph.py` for visualizing training outcomes and module performance. - Introduce `inference.py` as an example inference script for LLM interactions. - Add scripts for cloning training repositories and seeding databases from the training corpus. - Implement `verify_all.py` to run verification scripts in the code-review environment.902cd29
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feat: Implement validation for canonical fixture and add training suite
- Added `validate_canonical_fixture.py` to check required files and patterns in the sample project. - Created unit tests in `test_canonical_fixture.py` to ensure fixture validation works as expected. - Developed `test_phase8_training_api.py` to test training API endpoints and their responses. - Introduced `TrainingRunManager` and `WeightSafetyManager` in `run_manager.py` and `weights.py` for managing training runs and model weights. - Established a new directory structure under `training/` for organizing training-related code. - Documented the major refactor plan for transitioning to a deterministic ground truth and training suite in `phase-07-major-refactor-static-ground-truth-training-suite-plan.md`. - Outlined the completion plan for deterministic ground truth and agent training integration in `phase-08-deterministic-ground-truth-agent-training-completion-plan.md`.babc153