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name: training-and-generation description: "Inspect and operate OpenPrompt training, evaluation, generation, runner selection, device placement, checkpointing, few-shot, zero-shot, LM-BFF, and ProtoVerb workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Generation

Use this sub-skill when the task asks how to run, inspect, resume, test, or debug OpenPrompt training/generation jobs after the dataset, template, verbalizer, and package environment choices are known.

Start here:

• Use references/api-reference.md for runner APIs, device placement, config keys, checkpoint/logging semantics, and generation contracts.
• Use references/workflows.md for safe dry-runs, classification, generation, LM-BFF, ProtoVerb, few-shot, zero-shot, resume, and test recipes.
• Use references/troubleshooting.md when CUDA, model cache, dataset, checkpoint, generation length, teacher-forcing, few-shot sampling, LM-BFF, or ProtoVerb behavior fails.
• Use scripts/inspect_training_config.py to summarize a YAML config without loading models, loading datasets, or starting training.

Operating rules:

1. Dry-run first. Inspect experiments/*.yaml with scripts/inspect_training_config.py before running experiments/cli.py or a tutorial script.
2. Runner selection follows experiments/cli.py: classification with classification.auto_t or classification.auto_v uses LMBFFClassificationRunner; classification with verbalizer: proto_verbalizer uses ProtoVerbClassificationRunner; other classification uses ClassificationRunner; task: generation uses GenerationRunner.
3. Treat environment.num_gpus > 0 as a real CUDA request. OpenPrompt calls model.cuda() or wraps DataParallel; it does not automatically fall back to CPU when CUDA is unavailable.
4. BaseRunner writes TensorBoard and checkpoints under config.logging.path unless train.clean: True. The code writes checkpoints/last.ckpt and copies to best.ckpt on improved validation; the declared checkpoint.save_latest and checkpoint.save_best flags are not enforced by BaseRunner.
5. Generation training requires batches with tgt_text and correct teacher_forcing/predict_eos_token behavior. For evaluation/generation, pass generation kwargs deliberately; generation.max_length includes prompt/input tokens in the native generation API.
6. Few-shot and zero-shot behavior is owned by experiments/cli.py: few-shot loops over sampling_from_train.seed; zero-shot calls runner.test() without fitting.
7. Route dataset layout/config merging details to ../data-and-config-workflows/, prompt grammar/verbalizer design to ../template-verbalizer-design/, and top-level import/loading quickstarts to ../pipeline-basics/.
8. Large-model CUDA, UltraChat/accelerate, and PaddlePaddle tutorial variants are optional workflows. The minimum verified scope is CPU package/config/API inspection, not GPU training throughput or benchmark reproduction.