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Model: cx-cmu/repro-rephraser-4B
Source: Original Platform

README.md

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+---
+base_model:
+- Qwen/Qwen3-4B
+datasets:
+- cx-cmu/repro-rl-data
+language:
+- en
+tags:
+- pretraining
+pipeline_tag: text-generation
+library_name: transformers
+license: apache-2.0
+---
+
+This is the 4B rephraser from [RePro: Training Language Models to Faithfully Recycle the Web for Pretraining](https://huggingface.co/papers/2510.10681).
+
+The model is trained with RL from [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) to generate high-quality and faithful web rephrasings.
+
+Code: https://github.com/cxcscmu/RePro
+
+## Example Usage
+
+```python
+from vllm import LLM, SamplingParams
+import re
+
+# -----------------------
+# 1. Define model and params
+# -----------------------
+llm = LLM(model="cx-cmu/repro-rephraser-4B")
+
+sampling_params = SamplingParams(
+    temperature=1.0,
+    top_p=0.9,
+    max_tokens=2048,
+)
+
+# -----------------------
+# 2. Define the paraphrasing prompt
+# -----------------------
+template = """Your task is to read and paraphrase the provided text following these instructions:
+- Delete clearly irrelevant content:
+  - Website headers, navigation bars, or menu items (e.g., "Home | About | Contact")
+  - Unrelated HTTP links (e.g., ads, trackers, developer tools)
+  - Generic footers (e.g., contact info, privacy policies, unsubscribe links)
+  - Empty lines or decorative elements (e.g., "---")
+- Preserve all content that is relevant and meaningful:
+  - Informative or independently useful
+  - Related to the topic, even tangentially
+  - Provides context, background, or supporting value
+  - Includes technical terms, key concepts, factual details, reasoning, and examples
+- Handle mixed-relevance sentences carefully:
+  - Remove only the irrelevant fragment if the rest remains coherent
+  - Delete the whole sentence if the remainder loses meaning
+- Do not alter meaningful content unnecessarily:
+  - Only delete or modify when content is clearly meaningless or off-topic
+  - Preserve the original structure, logic, and depth of the text
+- Do not add explanations, notes, assumptions, or claims not found in the original text
+Here is the text:
+{TEXT}
+Task:
+After thoroughly reading the above text, paraphrase it in high-quality and clear English following the instructions.
+Start your response immediately with "Here is a paraphrased version:" and then provide the paraphrased text."""
+
+# -----------------------
+# 3. Prepare a sample conversation
+# -----------------------
+sample_text = """The Pittsburgh Steelers are a professional American football team based in Pittsburgh, Pennsylvania.
+They were established in 1933 and are one of the oldest franchises in the NFL."""
+
+conversation = [
+    {
+        "role": "system",
+        "content": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the questions. /no_think",
+    },
+    {
+        "role": "user",
+        "content": template.format(TEXT=sample_text),
+    },
+]
+
+# -----------------------
+# 4. Run vLLM inference
+# -----------------------
+output = llm.chat([conversation], sampling_params)
+response_text = output[0].outputs[0].text
+
+# -----------------------
+# 5. Extract paraphrased text
+# -----------------------
+match = re.search(r"Here is a paraphrased version:(.*)", response_text, re.DOTALL)
+if match:
+    paraphrased = match.group(1).strip()
+else:
+    paraphrased = response_text.strip()
+
+print("=== Paraphrased Output ===")
+print(paraphrased)
+```