Update README.md (#1)

- Update README.md (60af1bd0919fb9d0302af575bcb58cda7602bd36)


Co-authored-by: Kyle Shi <[email protected]>

README.md

@@ -1,3 +1,96 @@
----
-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 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}
+}
+```