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license: apache-2.0 |
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--- |
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# JT-Math-8B-Base |
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<p align="center"> |
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<a href="https://www.arxiv.org/abs/2507.19748" target="_blank"> |
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<img src="https://img.shields.io/badge/Paper-ArXiv-red"> |
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</a> |
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<a href="https://huggingface.co/JT-LM/JT-Math-8B-Base" target="_blank"> |
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<img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue"> |
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</a> |
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<a href="https://www.modelscope.cn/models/JiuTian-AI/JT-Math-8B-Base" target="_blank"> |
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<img src="https://img.shields.io/badge/%F0%9F%A4%96%20ModelScope-Models-blue"> |
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</a> |
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</p> |
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We are excited to introduce JT-Math-8B-Base: an 8-billion-parameter foundation model engineered for mathematical reasoning and the cornerstone of the JT-Math family. JT-Math-8B-Base was pre-trained on top of JT-Coder-8B-Base using an additional 210 billion tokens of high-quality mathematical and general-domain data. With a native 32,768-token context window, it provides a robust, scalable, and reproducible foundation for downstream fine-tuning—enabling researchers and developers to advance the frontier of math-centric AI applications. Technical details, training recipes, and reproducibility notes are available in our technical report. |
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## Model Downloads |
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We release the following models to support a wide range of applications. |
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| Model Name | Context Length | Hugging Face Link | ModelScope Link | Notes | |
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| ------------------- | -------------- | ---------------------------------------------------------- | ---------------------------------------------------------- | ---------------------------------------------------------- | |
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| JT-Math-8B-Base | 32K | [Link](https://huggingface.co/JT-LM/JT-Math-8B-Base) | [Link](https://www.modelscope.cn/models/JiuTian-AI/JT-Math-8B-Base) | The foundational base model. Ideal for custom fine-tuning. | |
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------ |
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## Evaluation Results |
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| Model | GSM8K | Math | CMath (zh) | Average | |
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| --------------------------- | ----- | ----- | ---------- | ------- | |
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| Qwen2.5-Base-32B | 92.8 | 57.7 | 85.4 | 78.6 | |
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| Llama-3.1-Base-405B | 89.0 | 53.8 | 77.4 | 73.4 | |
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| DeepSeek-Math-Base-7B | 64.2 | 36.2 | 71.7 | 57.4 | |
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| DeepSeek-Coder-V2-Lite-Base | 68.3 | 38.1 | 77.8 | 61.4 | |
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| Qwen2.5-Math-7B | 91.6 | 55.4 | 85.0 | 77.3 | |
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| *JT-Math-8B-Base* | 87.5 | 60.1 | 90.2 | *79.2* | |
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## How to Get Started |
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We provide a basic example of how to run inference with the `JT-Math-8B-Base` model. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "JT-LM/JT-Math-8B-Base" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto", |
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trust_remote_code=True, |
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) |
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prompt = "Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?" |
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text = f"Question:\n{prompt}\nAnswer:\n" |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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gen_kwargs = { |
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"do_sample": False, |
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"max_new_tokens": 8192, |
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} |
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generated_ids = model.generate( |
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**model_inputs, |
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**gen_kwargs |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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response = tokenizer.decode(output_ids, skip_special_tokens=True) |
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print("response:", response) |
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``` |
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## Citation |
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If you find our work useful, please consider citing our paper: |
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```latex |
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@article{jiutian-math2025, |
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title={JIUTIAN MATH: A MULTI-STAGE FRAMEWORK FOR ADVANCED MATHEMATICAL REASONING IN LARGE LANGUAGE MODELS}, |
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author={Yifan Hao, Fangning Chao, Yaqian Hao, Zhaojun Cui, Huan Bai, Haiyu Zhang, Yankai Liu, Chao Deng, Junlan Feng}, |
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journal={arXiv:2507.19748}, |
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year={2025} |
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} |
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``` |
