Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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 DeepSeekMath-7B-ScaleQuest
+
+<!-- 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 `DeepSeekMath-7B-ScaleQuest`
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "dyyyyyyyy/DeepSeekMath-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=""
+query_prompt="User:" + " "
+# {query}
+prompt_after_query="\n" + "Please reason step by step, and put your final answer within \\boxed{}." + "\n\n"
+resp_prompt="Assistant:" + " "
+prompt_before_resp=""
+# {resp}
+delim="<｜end▁of▁sentence｜>"
+
+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
+
+```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}
+}
+```