README.md

---
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 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="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}
}
```




```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]
```