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name: nlu-glue-adaptation description: "Construct and troubleshoot LoRA fine-tuning and evaluation workflows for RoBERTa or DeBERTa-v2 GLUE-style sequence classification." disable-model-invocation: true metadata: disco-role: operating license: MIT

NLU GLUE adaptation

Use this sub-skill for the repository's LoRA-aware sequence-classification workflow: model configuration, query/value projection replacement, GLUE task commands, adapter checkpoint transfer, and training-resource checks.

Route here

• Build a LoRA GLUE command for RoBERTa or DeBERTa-v2.
• Explain --apply_lora, --lora_r, --lora_alpha, and --lora_path.
• Port the repository's query/value adaptation into another Transformers version or a custom encoder.
• Diagnose missing checkpoints, label-head mismatches, or CUDA/distributed launch failures.

Start fast

1. Install the base model's Transformers and dataset dependencies in the target environment, then verify that the runner's --help works before downloading a model.
2. Choose the model family and task. The archived recipes use LoRA on query and value projections and leave key projections unchanged.
3. Set --apply_lora, a positive --lora_r, and --lora_alpha. Use --lora_path only when transferring an adapter trained on a compatible base architecture/task.
4. Start with one GPU or a dry command. Add --fp16 and distributed launch only after the model loads and a tiny batch succeeds.
5. Save output and logs in a new directory; do not overwrite a non-empty output directory unless resuming intentionally.

Generate a safe command without opening the archived shell launchers:

python scripts/build_glue_lora_command.py \
  --model roberta-base --task mnli --script run_glue.py --num-gpus 1

Model-specific defaults

• RoBERTa recipes generally use r=8, alpha=16, sequence length 512, and a higher learning rate than ordinary full fine-tuning.
• DeBERTa-v2 XXL recipes use r=16, alpha=32, sequence length 256, fp16, and distributed launch. Treat those as resource-heavy starting points, not universal hyperparameters.
• For MRPC, RTE, and STS-B transfer workflows, the archived recipes expect a LoRA-adapted MNLI checkpoint. Ensure the adapter path and base model match.

Reroute

• Layer constructor, trainable parameters, and state-dict details: use ../core-lora-api/SKILL.md.
• GPT-2 data-to-text training and decoding: use ../nlg-gpt2-adaptation/SKILL.md.

References

• Read GLUE workflows for flags, launcher patterns, task/data layouts, and checkpoint transfer.
• Read model integration for the exact query/value insertion behavior and porting checklist.
• Read troubleshooting before changing rank, alpha, batch size, or model code to fix a runtime failure.

Common request patterns

Use this sub-skill when the user asks to:

• adapt an existing RoBERTa/DeBERTa classification model with LoRA;
• translate an archived launcher into a safer single-device smoke command;
• attach an MNLI-trained LoRA checkpoint to MRPC, RTE, or STS-B;
• understand why a run_glue.py call needs --apply_lora, --lora_r, and --lora_alpha together; or
• trace a missing-key error back to a changed module path, task head, or model family.

Exit checklist

Before returning, confirm the answer states:

1. which projections receive LoRA and which stay unchanged;
2. the command arguments that must match between training and transfer;
3. whether the suggested run assumes CUDA, distributed launch, or a CPU smoke;
4. where the adapter checkpoint should be loaded; and
5. what failure signals imply a bad base model, bad head, or stale launcher.