license: apache-2.0
JT-Math-8B-Base
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.
Model Downloads
We release the following models to support a wide range of applications.
| Model Name | Length | Download | Notes |
|---|---|---|---|
| JT-Math-8B-Base | 32K | 🤗 | The base model. Continually pre-trained from JT-Coder-8B-Base. |
Evaluation Results
| Model | GSM8K | Math | CMath (zh) | Average |
|---|---|---|---|---|
| Qwen2.5-Base-72B | 91.5 | 62.12 | 84.5 | 79.4 |
| Llama-3.1-Base-405B | 89.0 | 53.8 | 77.4 | 73.4 |
| DeepSeek-Math-Base-7B | 64.2 | 36.2 | 71.7 | 57.4 |
| DeepSeek-Coder-V2-Lite-Base | 68.3 | 38.1 | 77.8 | 61.4 |
| InternLM2-Math-Base-20B | 68.2 | 30.4 | 65.9 | 54.8 |
| Qwen2.5-Math-7B | 91.6 | 55.4 | 85.0 | 77.3 |
| JT-Math-8B-Base | 88.0 | 58.1 | 90.0 | 78.7 |
How to Get Started
We provide a basic example of how to run inference with the JT-Math-8B-Base model.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Jiutian/JT-Math-8B-Base"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
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?"
text = f"Question:\n{prompt}\nAnswer:\n"
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
gen_kwargs = {
"do_sample": False,
"max_new_tokens": 8192,
}
generated_ids = model.generate(
**model_inputs,
**gen_kwargs
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print("response:", response)
Citation
If you find our work useful, please consider citing our paper:
@article{jiutian-math2025,
title={JIUTIAN MATH: A MULTI-STAGE FRAMEWORK FOR ADVANCED MATHEMATICAL REASONING IN LARGE LANGUAGE MODELS},
author={Authors},
journal={arXiv preprint arXiv:xxxx.xxxxx},
year={2025}
}