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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+license_link: https://huggingface.co/Xtra-Computing/XtraGPT-7B/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-7B-Instruct
+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 Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
+
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```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="""
+In this paper, we propose a metric-based probing method, namely, CAT-probing, to quantitatively evaluate how CodePTMs Attention scores relate to distances between AST nodes.
+First, to denoise the input code sequence in the original attention scores matrix, we classify the rows/cols by token types that are pre-defined by compilers,
+and then retain tokens whose types have the highest proportion scores to derive a filtered attention matrix (see Figure 1(b)).
+Meanwhile, inspired by the works (Wang et al., 2020; Zhu et al., 2022), we add edges to improve the connectivity of AST and calculate the distances between nodes corresponding to the selected tokens,
+which generates a distance matrix as shown in Figure 1(c). After that, we define CAT-score to measure the matching degree between the filtered attention matrix and the distance matrix.
+Specifically, the point-wise elements of the two matrices are matched if both the two conditions are satisfied:
+1) the attention score is larger than a threshold; 2) the distance value is smaller than a threshold. If only one condition is reached, the elements are unmatched.
+We calculate the CAT-score by the ratio of the number of matched elements to the summation of matched and unmatched elements.
+Finally, the CAT-score is used to interpret how CodePTMs attend code structure, where a higher score indicates that the model has learned more structural information.
+"""
+
+selected_content="""
+After that, we define CAT-score to measure the matching degree between the filtered attention matrix and the distance matrix.
+"""
+
+prompt ="""
+help me redefine cat-score 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]
+```
+
+
+```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="""
+After that, we define CAT-score to measure the matching degree between the filtered attention matrix and the distance matrix.
+"""
+
+prompt ="""
+help me redefine cat-score 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}
+}
+```