- Split the 22-assert config-values test into 3 focused tests
(task+data, training hyperparams, LoRA config) so a deliberate
example change surfaces in one targeted test, not a wall of asserts
- Add module docstring explaining why these tests lock the example state
- Add `from __future__ import annotations` (defensive; matches 14 other
test modules in the project)
- Rename `f` -> `fh` in _load_jsonl to avoid shadowing short name
- Drop asserts on secondary fields (warmup_ratio, weight_decay, scheduler,
logging_steps, etc.) -- they're tweakable knobs, not the example's
teaching points; test brittleness > coverage here
Add a working DPO (Direct Preference Optimization) example using the
current Pydantic config schema with Llama 3.1 8B Instruct and QLoRA.
- examples/configs/dpo_example.yaml: DPO config with all core training
and LoRA parameters, plus commented-out advanced options
- examples/data/dpo_sample.jsonl: 8 preference pairs in DPO format
with ShareGPT-style message lists for chosen/rejected
- tests/test_dpo_example.py: 7 tests validating config loading, field
values, data format detection, and data validation
- examples/README.md: document the new DPO with QLoRA example
Alpamys
Chinmaya Sahu