File size: 3,634 Bytes
d92a3ce |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
---
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-DeepSeekMath-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-DeepSeekMath-7B-QGen`
```python
from vllm import LLM, SamplingParams
model_name = "dyyyyyyyy/ScaleQuest-DeepSeekMath-7B-QGen"
pre_query_template = "<｜begin▁of▁sentence｜>User: "
stop_tokens = ["<｜begin▁of▁sentence｜>", "<｜end▁of▁sentence｜>"]
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}
}
```
|