Update README.md

README.md

@@ -9,129 +9,4 @@ tags:
 - chat
 library_name: transformers
 ---
-# XtraGPT-7B
-
-## Introduction
-
-XtraGPT is a series of LLMs for Human-AI Collaboration on Controllable Scientific Paper Refinement developed by NUS [Xtra Computing Group](https://github.com/Xtra-Computing).
-
-## Requirements
-
-The code of XtraGPT has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
-
-## Quickstart
-
-```python
-from openai import OpenAI
-model_name = "Xtra-Computing/XtraGPT-7B"
-client = OpenAI(
-    base_url="http://localhost:8088/v1",
-    api_key="sk-1234567890"
-)
-
-paper_content="markdown"
-selected_content="Through a detailed analysis of reasoning requirements across evaluation tasks, we reveal a negative correlation between SFT performance gains and the proportion of reasoning-demanding samples—highlighting the limitations of SFT in such scenarios."
-
-prompt = "help me modify based on the context."
-
-content = f"""
-Please improve the selected content based on the following. Act as an expert model for improving articles **PAPER_CONTENT**.\n
-The output needs to answer the **QUESTION** on **SELECTED_CONTENT** in the input. Avoid adding unnecessary length, unrelated details, overclaims, or vague statements.
-Focus on clear, concise, and evidence-based improvements that align with the overall context of the paper.\n
-<PAPER_CONTENT> {paper_content} </PAPER_CONTENT>\n <SELECTED_CONTENT> {selected_content} </SELECTED_CONTENT>\n <QUESTION> {prompt} </QUESTION>\n
-"""
-
-response = client.chat.completions.create(
-    model="xtragpt",
-    messages=[{"role": "user", "content": content}],
-    temperature=0.7,
-    max_tokens=16384
-)
-print(response.choices[0].message.content)
-
-```
-
-## Citation
-
-If you find our work helpful, feel free to give us a cite.
-
-```
-@misc{xtracomputing2025xtraqa,
-    title = {XtraQA},
-    url = {https://huggingface.co/Xtra-Computing/XtraGPT-7B},
-    author = {Xtra Computing Group},
-    year = {2025}
-}
-@article{xtracomputing2025xtragpt,
-      title={XtraGPT: LLMs for Human-AI Collaboration on Controllable Scientific Paper Refinement}, 
-      author={Xtra Computing Group},
-      journal={arXiv preprint arXiv:abcdefg},
-      year={2025}
-}
-```
-
-
-
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "Xtra-Computing/XtraGPT-7B"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-paper_content="""
-The rise of Large Language Models (LLMs) as evaluators offers a scalable alternative to human annotation, yet existing Supervised Fine-Tuning (SFT) for judges approaches often fall short in domains requiring complex reasoning. In this work, we investigate whether LLM judges truly benefit from enhanced reasoning capabilities. Through a detailed analysis of reasoning requirements across evaluation tasks, we reveal a negative correlation between SFT performance gains and the proportion of reasoning-demanding samples—highlighting the limitations of SFT in such scenarios. To address this, we introduce \textbf{JudgeLRM}, a family of judgment-oriented LLMs trained using reinforcement learning (RL) with judge-wise, outcome-driven rewards. JudgeLRM models consistently outperform both SFT-tuned and state-of-the-art reasoning models. Notably, JudgeLRM-3B surpasses GPT-4, and JudgeLRM-7B outperforms DeepSeek-R1 by 2.79\% in F1 score, particularly excelling in judge tasks requiring deep reasoning.
-"""
-
-selected_content="""
-Through a detailed analysis of reasoning requirements across evaluation tasks, we reveal a negative correlation between SFT performance gains and the proportion of reasoning-demanding samples—highlighting the limitations of SFT in such scenarios.
-"""
-prompt ="""
-help me modify based on the context.
-"""
-
-content = f"""
-Please improve the selected content based on the following. Act as an expert model for improving articles **PAPER_CONTENT**.\n
-The output needs to answer the **QUESTION** on **SELECTED_CONTENT** in the input. Avoid adding unnecessary length, unrelated details, overclaims, or vague statements.
-Focus on clear, concise, and evidence-based improvements that align with the overall context of the paper.\n
-
-<PAPER_CONTENT>
-{paper_content}
-</PAPER_CONTENT>\n
-
-<SELECTED_CONTENT>
-{selected_content}
-</SELECTED_CONTENT>\n
-
-<QUESTION>
-{prompt}
-</QUESTION>\n
-"""
-
-messages = [
-    {"role": "user", "content": content}
-]
-
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=512
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
-
-
-