Update README.md

README.md

@@ -1,202 +1,132 @@
 ---
 base_model: meta-llama/Llama-3.2-3B-Instruct
 library_name: peft
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Adaptively-tuned Llama-3.2-3B 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
 
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+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:** meta-llama/Llama-3.2-3B-Instruct
+
+## Get Started
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+# Load the base model
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
+
+# Load the LoRA adapter
+model = PeftModel.from_pretrained(model, "DDiaa/Unigram-Llama-3.2-3B")
+
+# 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
+```
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
+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
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+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 section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+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
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- 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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
+- 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
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
 
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Citation
 
 **BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+```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
 
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.13.2
+For questions about this model, please file an issue on the GitHub repository: https://github.com/ML-Watermarking/ada-llm-wm