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name: "classification-training" description: "Plan Qwen-VL sequence-classification training, labels, losses, metrics, and early stopping." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Classification Training

Use this sub-skill for Qwen-VL sequence classification, class-imbalance losses, custom heads, and early stopping.

Covers

• train_cls.py command planning.
• Classification dataset shape and label mapping.
• Cross-entropy, focal loss, and class-balanced losses.
• MLP head settings and label metrics.
• Eval hooks and early stopping.
• DeepSpeed launch planning for classification.

Excludes

• SFT, DPO, and GRPO training.
• Adapter merge or serving.
• Generic image augmentation not tied to this repo.

Read first

• ../../references/workflow-map.md for route confirmation.
• ../../references/data-formats.md for media/path conventions that also apply here.
• ../../references/cli-reference.md for the shared flag surface.
• references/losses.md for class-aware losses and head choices.
• references/troubleshooting.md for classification-specific failures.
• scripts/classification_command.py for a safe command builder.

Typical user requests

• "Train a classifier on Qwen-VL."
• "How do I choose the classification loss?"
• "How do I add a tiny MLP head?"
• "How do I stop training on poor validation improvements?"

Workflow

1. Confirm the label set and prompt format.
2. Decide whether the dataset needs eval records.
3. Pick a loss function based on imbalance and the user’s metric goal.
4. Decide whether LoRA or head-only tuning is needed.
5. Emit the command with the bundled command builder.

Decision rules

• Default labels in the repo are A -> 0 and B -> 1.
• If the dataset omits prompt, the repo injects a default user message.
• The trainer can use early stopping when validation stops improving.
• LoRA classification saves the classifier head with modules_to_save.
• The repo disables Liger for sequence-classification wrappers at runtime even if the flag is present.

Safe command builder

Start with the bundled helper:

python scripts/classification_command.py --help
python scripts/classification_command.py --model-id Qwen/Qwen2.5-VL-3B-Instruct --data-path data/train.json --image-folder data/images --output-dir outputs/cls

If you need more detail

Read references/losses.md for the loss choices and references/troubleshooting.md for label, head, and metric issues.