JT-Math-8B-Thinking

We are excited to present JT-Math-8B-Thinking, a powerful 8-billion parameter model from the JT-Math series, engineered specifically for advanced mathematical reasoning and complex problem-solving. Fine-tuned on a carefully curated, bilingual (English and Chinese) dataset, ensuring high performance on mathematical tasks in both languages. This model has 32,768-token context window, allowing it to process and reason over extensive and intricate problem descriptions. JT-Math-8B-Thinking has been meticulously optimized to tackle difficult mathematical challenges, achieving state-of-the-art (SOTA) performance on key reasoning benchmarks when compared against models of a similar parameter class. Its development process involves a multi-stage training pipeline designed to maximize its reasoning capabilities. For full transparency and reproducibility, please refer to our technical report which details our training recipe and pipeline.

Model Details

The performance of JT-Math-8B-Thinking stems from a meticulous, multi-stage training approach aimed at tackling complex mathematical challenges with state-of-the-art accuracy. Building on the JT-Math-8B-Base model, its training pipeline involved Supervised Fine-Tuning (SFT) using a high-quality, bilingual dataset of intricate math problems. This SFT phase leveraged the model's native 32,768-token context window, enabling it to comprehend lengthy premises, multi-step instructions, and problems with extensive background information right from the start. Following SFT, an advanced Reinforcement Learning (RL) phase further refined its reasoning capabilities. This RL process employed a multi-stage curriculum, gradually introducing problems of increasing difficulty, and was specifically engineered to boost the model's focus and accuracy across the entire 32K context window, ensuring the coherence and precision of even the longest reasoning chains.

Model Downloads

We release the following models to support a wide range of applications.

Model Name	Context Length	Hugging Face Link	ModelScope Link	Notes
JT-Math-8B-Thinking	32K	Link	Link	The premier model for complex, long-context reasoning.

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Evaluation Results

JT-Math-8B-Thinking achieves competitive performance among open-source models in the ~8B class on mathematical reasoning benchmarks.

How to Get Started

This example shows how to use the JT-Math-8B-Thinking model to solve math problems.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "JT-LM/JT-Math-8B-Thinking"

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?"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

gen_kwargs = {
    "do_sample": True,
    "temperature": 0.65,  # Recommended temperature is 0.65
    "max_new_tokens": 32768,
}
generated_ids = model.generate(
    **model_inputs,
    **gen_kwargs
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

raw_content = tokenizer.decode(output_ids, skip_special_tokens=True)
if "</think>" in raw_content:
    thinking_content = raw_content.rsplit("</think>", 1)[0].strip("\n")
    content = raw_content.rsplit("</think>", 1)[1].strip("\n")
else:
    think_content = raw_content
    content = ""

print("raw content:", raw_content)
print("thinking content:", thinking_content)
print("content:", content)

Citation

If you use JT-Math-8B-Thinking in your research, please cite our work:

@article{jiutian-math2025,
  title={JIUTIAN MATH: A MULTI-STAGE FRAMEWORK FOR ADVANCED MATHEMATICAL REASONING IN LARGE LANGUAGE MODELS},
  author={Yifan Hao, Fangning Chao, Yaqian Hao, Zhaojun Cui, Huan Bai, Haiyu Zhang, Yankai Liu, Chao Deng, Junlan Feng},
  journal={arXiv:2507.19748},
  year={2025}
}