refactor(tests): polish DPO example tests from PR #48

- 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

tests/test_dpo_example.py

@@ -1,4 +1,12 @@
-"""Tests for the DPO example config and sample data."""
+"""Tests for the DPO example config and sample data.
+
+These tests lock the example config + data to a working state so users who
+follow README steps never hit a broken example. Values are asserted in
+logical groups so a deliberate example change surfaces in one focused test
+rather than a 22-line wall.
+"""
+
+from __future__ import annotations
 
 import json
 from pathlib import Path
@@ -19,36 +27,36 @@ def test_dpo_example_config_loads():
     assert isinstance(cfg, SoupConfig)
 
 
-def test_dpo_example_config_values():
-    """dpo_example.yaml should have the expected field values."""
+def test_dpo_example_task_and_data():
+    """Core task + data wiring: what matters for DPO to run at all."""
     cfg = load_config(DPO_CONFIG_PATH)
-    assert cfg.base == "meta-llama/Llama-3.1-8B-Instruct"
     assert cfg.task == "dpo"
-    assert cfg.data.train == "examples/data/dpo_sample.jsonl"
     assert cfg.data.format == "dpo"
-    assert cfg.data.max_length == 2048
+    assert cfg.data.train == "examples/data/dpo_sample.jsonl"
+    assert cfg.base == "meta-llama/Llama-3.1-8B-Instruct"
+
+
+def test_dpo_example_training_hyperparams():
+    """Training hyperparameters match the example's advertised config."""
+    cfg = load_config(DPO_CONFIG_PATH)
     assert cfg.training.epochs == 3
     assert cfg.training.lr == 5e-6
     assert cfg.training.dpo_beta == 0.1
     assert cfg.training.quantization == "4bit"
     assert cfg.training.batch_size == 4
     assert cfg.training.gradient_accumulation_steps == 4
-    assert cfg.training.warmup_ratio == 0.1
-    assert cfg.training.weight_decay == 0.01
-    assert cfg.training.max_grad_norm == 1.0
-    assert cfg.training.optimizer == "adamw_torch"
-    assert cfg.training.scheduler == "cosine"
-    assert cfg.training.logging_steps == 10
-    assert cfg.training.save_steps == 100
+
+
+def test_dpo_example_lora_config():
+    """LoRA config matches the example's advertised rank/alpha."""
+    cfg = load_config(DPO_CONFIG_PATH)
     assert cfg.training.lora.r == 16
     assert cfg.training.lora.alpha == 32
-    assert cfg.training.lora.dropout == 0.05
     assert cfg.training.lora.target_modules == "auto"
-    assert cfg.output == "./output_dpo_example/"
 
 
 def test_dpo_sample_data_exists():
-    """dpo_sample.jsonl should exist and be non-empty."""
+    """dpo_sample.jsonl should exist and have the issue-#4 minimum of 5+ pairs."""
     assert DPO_DATA_PATH.exists()
     data = _load_jsonl(DPO_DATA_PATH)
     assert len(data) >= 5, "Expected at least 5 preference pairs"
@@ -92,5 +100,5 @@ def test_dpo_sample_data_converts():
 
 def _load_jsonl(path: Path) -> list[dict]:
     """Load a JSONL file into a list of dicts."""
-    with open(path, encoding="utf-8") as f:
-        return [json.loads(line) for line in f if line.strip()]
+    with open(path, encoding="utf-8") as fh:
+        return [json.loads(line) for line in fh if line.strip()]