tiararodney/sekft
2
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

todo(16): open

Browse source
This commit is contained in:
Tiara Rodney 2026-06-18 16:46:10 +02:00
parent a76470e55d
commit 87cfccd54e
Signed by: tiara
GPG key ID: 5CD8EC1D46106723
1 changed files with 23 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

23
TODO
View file

@ -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 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 fake-tokenizer test returned a bare list and missed this, so add a
BatchEncoding-returning fake and assert the mask matches. 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.