---
license: apache-2.0
datasets:
- dyyyyyyyy/ScaleQuest-Math
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-generation
---
<p align="center"><h2 align="center">Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch</h2></p>

# Model Card for ScaleQuest-Qwen2-Math-7B-QGen

<!-- Provide a quick summary of what the model is/does. -->

We introduce ScaleQuest, a scalable and novel data synthesis method that utilizes small-size open-source models to generate questions from scratch without the need for seed data with complex augmentation constraints.

* 📑 Project Page: [https://scalequest.github.io](https://scalequest.github.io/)
* 💻 Code: [https://github.com/yyDing1/ScaleQuest](https://github.com/yyDing1/ScaleQuest/)
* 📖 Paper: [Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch](https://arxiv.org/abs/2410.18693)
* 💾 Models in the 🤗 HuggingFace Hub: [ScaleQuest-Models](https://huggingface.co/collections/dyyyyyyyy/scalequest-670a7dc2623c91990f28913b)

<p align="center">
<img src="https://github.com/yyDing1/ScaleQuest/raw/main/img/results.png">
</p>

## Datasets & Models

Math Dataset: [link](https://huggingface.co/datasets/dyyyyyyyy/ScaleQuest-Math)

We release two question generator models and four problem-solving models.

| Model | Type | MATH | Olympiad Bench | 🤗 HuggingFace<br />Download Link |
| - | :-: | :-: | :-: | :-: |
| ScaleQuest-DeepSeekMath-7B-QGen | question generator | - | - | [link](https://huggingface.co/dyyyyyyyy/ScaleQuest-DeepSeekMath-7B-QGen)
| ScaleQuest-Qwen2-Math-7B-QGen | question generator | - | - | [link](https://huggingface.co/dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen)
| Mistral-7B-ScaleQuest | problem solver | 62.9 | 26.8 | [link](https://huggingface.co/dyyyyyyyy/Mistral-7B-ScaleQuest) |
| Llama3-8B-ScaleQuest | problem solver | 64.4 | 25.3 | [link](https://huggingface.co/dyyyyyyyy/Llama3-8B-ScaleQuest) |
| DeepSeekMath-7B-ScaleQuest | problem solver | 66.6 | 29.9 | [link](https://huggingface.co/dyyyyyyyy/DeepSeekMath-7B-ScaleQuest) |
| Qwen2-Math-7B-ScaleQuest | problem solver | 73.4 | 38.5 | [link](https://huggingface.co/dyyyyyyyy/Qwen2-Math-7B-ScaleQuest) |

## Demo usage

Below is an example using `ScaleQuest-Qwen2-Math-7B-QGen`
```python
from vllm import LLM, SamplingParams

model_name = "dyyyyyyyy/ScaleQuest-Qwen2-Math-7B-QGen"

pre_query_template = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n"
stop_tokens = ["<|im_start|>", "<|im_end|>", "<|endoftext|>"]

llm = LLM(
    model=model_name,
    tokenizer=model_name,
    tensor_parallel_size=1,
    max_model_len=4096,
    enable_prefix_caching=True,
    trust_remote_code=True,
    swap_space=16,
    gpu_memory_utilization=0.95,
)
sampling_params = SamplingParams(
    n=4,
    max_tokens=1024,
    temperature=1.0,
    top_p=0.99,
    stop=stop_tokens,
)

outputs = llm.generate(pre_query_template, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    for idx, generated_output in enumerate(output.outputs):
        generated_text = generated_output.text
        print(f"Sample {idx + 1}:")
        print(f"Prompt: {prompt!r}")
        print(f"Generated text: {generated_text!r}")
        print("-" * 50)

```

## Citation

```bibtex
@article{ding2024unleashing,
    title={Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch}, 
    author={Ding, Yuyang and Shi, Xinyu and Liang, Xiaobo and Li, Juntao and Zhu, Qiaoming and Zhang, Min},
    journal={https://arxiv.org/abs/2410.18693}, 
    year={2024}
}
```