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name: ptuning description: "Guides ChatGLM2-6B P-Tuning v2, chat fine-tuning data, prediction commands, prefix/full checkpoint loading, and optional DeepSpeed fine-tuning." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

ChatGLM2-6B P-Tuning and Fine-Tuning

Use this route when the task involves P-Tuning v2, chat fine-tuning data, ADGEN-style data, bundled P-Tuning runner arguments, prefix checkpoint loading, prediction, or optional full-parameter DeepSpeed fine-tuning. Use chat-and-demos for ordinary base-model chat and api-serving when a prepared model should be served.

Safe workflow

1. Validate data before launching torchrun: python sub-skills/ptuning/scripts/validate_ptuning_data.py --file train.json --format chat --history-column history.
2. Build a command string without executing it from the generated skill root: python sub-skills/ptuning/scripts/build_ptuning_command.py train --train-file train.json --validation-file dev.json --output-dir output/run.
3. Confirm model weights/cache, CUDA availability, and the intended checkpoint type. P-Tuning v2 saves a prefix encoder, not a full model; full fine-tuning checkpoints load differently.
4. Run expensive training/evaluation only after checking data columns, pre_seq_len, quantization, GPU memory, and output paths.

Read data-formats.md for ADGEN/chat schemas, command-templates.md for launch patterns, checkpoint-workflows.md for prefix vs full checkpoints, and troubleshooting.md for schema, OOM, DeepSpeed, quantization, and Gradio issues.

Boundaries

This sub-skill distills the repository's P-Tuning scripts into bundled references, validators, command builders, and a bundled runner under scripts/ptuning_runner/. It does not train or evaluate as a smoke test, does not download ADGEN/model weights, and does not make DeepSpeed a required dependency. Full training, prediction, and web demo launch are GPU/model/data dependent and should be treated as deliberate runtime actions, not verification shortcuts.