todo(16): open

TODO

@@ -271,3 +271,26 @@ Description: build_masked_example assumed apply_chat_template returns a flat
              the result is dict-like, and use it for both renders. The
              fake-tokenizer test returned a bare list and missed this, so add a
              BatchEncoding-returning fake and assert the mask matches.
+
+--ISSUE
+Content-Type: application/issue
+ID: 16
+Type: bugfix
+Title: generation operators pass BatchEncoding to generate (transformers 5.x)
+Status: open
+Priority: high
+Created: 2026-06-18
+Module: sekft
+Relationships: 
+Description: The same transformers 5.x return-type change that broke
+             build_masked_example (#15) also breaks the generation path:
+             apply_chat_template(add_generation_prompt=True,
+             return_tensors='pt') returns a BatchEncoding, and eval.py and
+             resident.py pass it straight to model.generate(), which does
+             inputs_tensor.shape[0] -> AttributeError (the holdout eval crashed
+             here on scenario 1). #15 only fixed the trainer. Factor the id
+             extraction into a shared _input_ids helper, add
+             render_prompt_ids(tokenizer, messages, device) in sft.py, and use
+             it in both operators. Add a unit test for _input_ids covering the
+             BatchEncoding and bare-sequence cases. This is the sweep I should
+             have done at #15.