Model Card for Mistral-7B-ScaleQuest
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.
| Model | Type | MATH | Olympiad Bench | 🤗 HuggingFaceDownload Link |
|---|---|---|---|---|
| ScaleQuest-DeepSeekMath-7B-QGen | question generator | - | - | link |
| ScaleQuest-Qwen2-Math-7B-QGen | question generator | - | - | link |
| Mistral-7B-ScaleQuest | problem solver | 62.9 | 26.8 | link |
| Llama3-8B-ScaleQuest | problem solver | 64.4 | 25.3 | link |
| DeepSeekMath-7B-ScaleQuest | problem solver | 66.6 | 29.9 | link |
| Qwen2-Math-7B-ScaleQuest | problem solver | 73.4 | 38.5 | link |
Demo usage
Below is an example using Mistral-7B-ScaleQuest
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "dyyyyyyyy/Mistral-7B-ScaleQuest"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
question = "Find the value of $x$ that satisfies the equation $4x+5 = 6x+7$."
sys_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request." + "\n\n"
query_prompt = "### Instruction:" + "\n"
# {query}
prompt_after_query = "\n\n"
resp_prompt = "### Response:" + "\n"
prompt_before_resp = ""
# {resp}
delim = "\n\n"
prefix_prompt = f"{query_prompt}{question}{prompt_after_query}{resp_prompt}{prompt_before_resp}".rstrip(" ")
full_prompt = sys_prompt + delim.join([prefix_prompt])
# print(full_prompt)
inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True))
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}
}