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What does this PR do? This PR adds support for Direct Preference Optimization (DPO) training via the existing HuggingFace inline provider. It introduces a new DPO training recipe, config schema updates, dataset integration, and end-to-end testing to support preference-based fine-tuning with TRL. Test Plan Added integration test: tests/integration/post_training/test_post_training.py::TestPostTraining::test_preference_optimize Ran tests on both CPU and CUDA environments --------- Co-authored-by: Ubuntu <ubuntu@ip-172-31-43-83.ec2.internal> Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com> |
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| .. | ||
| apis | ||
| cli | ||
| core | ||
| models | ||
| providers | ||
| strong_typing | ||
| templates | ||
| testing | ||
| ui | ||
| __init__.py | ||
| env.py | ||
| log.py | ||
| schema_utils.py | ||