Create README.md

README.md

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+---
+base_model: Qwen/Qwen2.5-3B-Instruct
+library_name: peft
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Adaptively-tuned Qwen2.5-3B Paraphraser
+
+This model is an adaptively fine-tuned version of Qwen2.5-3B-Instruct optimized to evade the Unigram watermarking method while preserving text quality. It serves as a paraphrasing model that maintains semantic meaning while modifying the statistical patterns used for watermark detection.
+
+## Model Details
+
+### Model Description
+
+This model is a fine-tuned version of Qwen2.5-3B-Instruct that has been optimized using Direct Preference Optimization (DPO) to evade the [Unigram watermarking method](https://arxiv.org/abs/2306.17439) described in Zhao et. al (2023). The model preserves text quality while modifying the statistical patterns that watermarking methods rely on for detection.
+
+- **Model type:** Decoder-only transformer language model
+- **Language(s):** English
+- **Finetuned from model:** Qwen/Qwen2.5-3B-Instruct
+
+## Get Started
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+# Load the base model
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
+
+# Load the LoRA adapter
+model = PeftModel.from_pretrained(model, "DDiaa/Unigram-Qwen2.5-3B")
+
+# Prepare the prompt
+
+system_prompt = (
+    "You are an expert copy-editor. Please rewrite the following text in your own voice and paraphrase all "
+    "sentences.\n Ensure that the final output contains the same information as the original text and has "
+    "roughly the same length.\n Do not leave out any important details when rewriting in your own voice. Do "
+    "not include any information that is not present in the original text. Do not respond with a greeting or "
+    "any other extraneous information. Skip the preamble. Just rewrite the text directly."
+)
+
+def paraphrase_text(text):
+    # Prepare prompt
+    prompt = tokenizer.apply_chat_template(
+        [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": f"\n[[START OF TEXT]]\n{text}\n[[END OF TEXT]]"},
+        ],
+        tokenize=False,
+        add_generation_prompt=True,
+    ) + "[[START OF PARAPHRASE]]\n"
+    
+    # Generate paraphrase
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=1.0,
+        do_sample=True,
+        pad_token_id=tokenizer.pad_token_id
+    )
+    
+    # Post-process output
+    paraphrased = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    paraphrased = paraphrased.split("[[START OF PARAPHRASE]]")[1].split("[[END OF")[0].strip()
+    
+    return paraphrased
+```
+
+
+## Uses
+
+### Direct Use
+
+The model is designed for research purposes to:
+1. Study the robustness of watermarking methods
+2. Evaluate the effectiveness of adaptive attacks against content watermarks
+3. Test and develop improved watermarking techniques
+
+### Downstream Use
+
+The model can be integrated into:
+- Watermark robustness evaluation pipelines
+- Research frameworks studying language model security
+- Benchmark suites for watermarking methods
+
+### Out-of-Scope Use
+
+This model should not be used for:
+- Production environments requiring watermark compliance
+- Generating deceptive or misleading content
+- Evading legitimate content attribution systems
+- Any malicious purposes that could harm individuals or society
+
+## Bias, Risks, and Limitations
+
+- The model inherits biases from the base Qwen2.5-3B-Instruct model
+- Performance varies based on text length and complexity
+- Evasion capabilities may be reduced against newer watermarking methods
+- May occasionally produce lower quality outputs compared to the base model
+- Limited to English language texts
+
+### Recommendations
+
+- Use only for research and evaluation purposes
+- Always maintain proper content attribution
+- Monitor output quality metrics
+- Consider ethical implications when studying security measures
+- Use in conjunction with other evaluation methods
+
+
+
+## Citation
+
+**BibTeX:**
+```bibtex
+@article{diaa2024optimizing,
+  title={Optimizing adaptive attacks against content watermarks for language models},
+  author={Diaa, Abdulrahman and Aremu, Toluwani and Lukas, Nils},
+  journal={arXiv preprint arXiv:2410.02440},
+  year={2024}
+}
+```
+
+## Model Card Contact
+
+For questions about this model, please file an issue on the GitHub repository: https://github.com/ML-Watermarking/ada-llm-wm