* Studio: fail fast on an invalid first training batch
Training a base vision-language model (e.g. Qwen/Qwen2-VL-7B or
unsloth/Qwen2-VL-7B) on a conversational image dataset crashed on the first
step with 'Expected ... Long, Int; but got torch.cuda.FloatTensor (embedding)'.
Root cause: the base model's chat template is a flat, media-only template that
renders to an empty string for role-based messages, so UnslothVisionDataCollator
hands the processor empty text, the processor returns empty input_ids, torch
defaults the empty tensor to float32, and the embedding lookup rejects it.
Add a preflight that runs one real batch through the trainer's own tokenization
and collation right before train(), and stops the run with an actionable message
when input_ids is empty or non-integer (pointing to the instruction-tuned variant
for the base-model case). Faithful across text, vision and audio-VLM paths, and
never blocks a run whose first batch is valid.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim comments in the training preflight
* Stub unsloth/trl in preflight test so backend CI collection passes
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
