Instructions to use WizardWang01/depo-paraphrase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use WizardWang01/depo-paraphrase with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "WizardWang01/depo-paraphrase") - Notebooks
- Google Colab
- Kaggle
DEPO-Paraphrase
DEPO โ Detector-Evasive Paraphrase via Constrained Policy Optimization
LoRA adapter on Qwen/Qwen3-4B-Instruct-2507. Trained with constrained RL using the MAGE detector reward and BERTScore semantic reward (CRL target ฯ = 0.85, checkpoint-750).
Usage
from inference import load_paraphrase_model
pm = load_paraphrase_model("WizardWang01/depo-paraphrase")
out = pm.rewrite("Your text here.")
print(out)
pip install torch transformers peft accelerate
python inference.py --adapter_path WizardWang01/depo-paraphrase
Generation defaults
| Parameter | Value |
|---|---|
| Prompt template | preserve-meaning paraphrase (eval_rl) |
| max_new_tokens | 512 |
| temperature | 0.9 |
| top_p | 0.95 |
| torch_dtype | bfloat16 |
Intended use
Research and evaluation only. Not for academic dishonesty, spam, or circumventing platform policies.
Citation
If you use this model, please cite the DEPO paper (Detector-Evasive Paraphrase via Constrained Policy Optimization).
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Model tree for WizardWang01/depo-paraphrase
Base model
Qwen/Qwen3-4B-Instruct-2507