bugfix(16): operators must not feed a BatchEncoding to model.generate

The transformers 5.x return-type change behind #15 also breaks generation:
apply_chat_template(add_generation_prompt=True, return_tensors="pt") returns a
BatchEncoding, and eval.py + resident.py passed it to model.generate, which does
inputs.shape[0] -> AttributeError (the holdout eval crashed on scenario 1). #15
fixed only the trainer. Factor a shared _input_ids helper and a render_prompt_ids
function; both operators use it. Tests cover _input_ids for both shapes and
render_prompt_ids.

tests/unit/test_sft.py

@@ -86,6 +86,21 @@ def test_mask_handles_batchencoding_return() -> None:
             == sft.build_masked_example(raw, FakeTok()))
 
 
+def test_input_ids_extracts_from_batchencoding_or_passthrough() -> None:
+    # BatchEncoding (transformers 5.x) -> its input_ids; bare list/tensor (4.x) -> itself
+    assert sft._input_ids({"input_ids": [1, 2, 3], "attention_mask": [1, 1, 1]}) == [1, 2, 3]
+    assert sft._input_ids([4, 5, 6]) == [4, 5, 6]
+
+
+def test_render_prompt_ids_normalises_and_appends_generation_prompt() -> None:
+    # the generation operators rely on this: fold + append <assistant>, return ids
+    # (not a BatchEncoding) so model.generate doesn't choke on a dict.
+    raw = [{"role": "system", "content": "orient"}, {"role": "user", "content": "go"}]
+    ids = sft.render_prompt_ids(FakeTok(), raw)
+    assert ids[-1] == "<assistant>"                 # generation prompt appended
+    assert {"orient", "go"} <= set(ids)             # system folded into the user turn
+
+
 def test_mask_raises_on_non_additive_template() -> None:
     class BadTok:
         def apply_chat_template(self, msgs: list[dict[str, str]], add_generation_prompt: bool = False,