THUDM/P-tuning-v2

Health v2 · maintenance only

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Adoption not part of the score

2078 stars · 212 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

P-tuning v2 is a Python implementation of deep prompt tuning, applying trainable continuous prompts at every transformer layer so prompt tuning matches full fine-tuning performance on small/medium models and hard tasks like sequence tagging. It accompanies the ACL 2022 paper and includes reproduction scripts, hyperparameter search tooling, and a text retrieval variant.

Use cases

• fine-tune BERT or RoBERTa with frozen backbone weights using trainable prompts
• adapt pretrained language models to sequence tagging tasks cheaply
• reproduce the P-Tuning v2 paper results on consumer GPUs
• run hyperparameter search for prompt tuning configurations
• train parameter-efficient neural text retrievers
• tune GLM models without full fine-tuning

When to choose

• you want parameter-efficient tuning comparable to full fine-tuning on small/medium models
• you need to reproduce the ACL 2022 P-Tuning v2 experiments
• GPU memory is limited and freezing the backbone is acceptable
• you work with sequence tagging or SuperGLUE-style NLU tasks

When to avoid

• you need a maintained production training framework with broad model support
• you want LoRA or other newer PEFT methods with active ecosystem support
• you need full fine-tuning rather than prompt-based adaptation
• you require up-to-date compatibility with recent PyTorch and transformer versions

Facets

library · maturity maintenance

machine-learning llm-training nlp machine-learning deep-learning large-language-models python prompt-tuning parameter-efficient-fine-tuning p-tuning prefix-tuning research-code pytorch pretrained-language-models natural-language-processing linux gpu

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