Add `soup data doctor` and `soup data lint`, killing the top *silent*
fine-tune failures before a single training step: EOS-missing-from-labels
(the #1 "model never stops generating" bug), BOS duplication, no-system-role
templates, and preference-data length bias (the #1 silent DPO degradation) —
none of which any competitor (Unsloth/Axolotl/LlamaFactory) checks for.
- utils/data_doctor.py: 8-check chat-template compat report over a
tokenizer + sampled rows, OK/MINOR/MAJOR taxonomy mirroring diagnose;
--show-mask N renders per-token trained/masked colouring through the
SAME masking dispatch (_build_row_labels) the report itself uses, so
the two can never disagree about what's actually trained.
- utils/data_lint.py: preference-data linter (dpo/orpo/simpo/ipo/bco/kto)
— length bias (Cohen's d), label imbalance, near-duplicates (MinHash),
identical chosen==rejected pairs, prompt leakage.
- commands/data_doctor.py: Typer layer for both commands; strips C0
control bytes before untrusted dataset content reaches the terminal.
- commands/diagnose.py: hardens the --evidence loader against a TOCTOU
symlink swap (O_NOFOLLOW + fstat-on-open-fd), backporting the pattern
soup ship shipped in v0.71.25 (closes v0.71.25 known-limitation (4)).
Live smoke against the real HuggingFaceTB/SmolLM2-135M-Instruct tokenizer
(Windows + RTX 3050) found and fixed two genuine bugs beyond the synthetic
fixtures: the EOS check needed to span-search the whole trained region
(not just the last token), and two apply_chat_template call sites needed
a broad except Exception for jinja2.exceptions.TemplateError.
+173 tests (14788 -> 15042). 5 sequential ECC reviews, every finding fixed.
Alpamys