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

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
• 💻 Code: https://github.com/yyDing1/ScaleQuest
• 📖 Paper: Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
• 💾 Models in the 🤗 HuggingFace Hub: ScaleQuest-Models

Datasets & Models

Math Dataset: link

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

Demo usage

Below is an example using ScaleQuest-Qwen2-Math-7B-QGen

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

@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}
}