You can use K2-Think with Transformers. If you use transformers.pipeline, it will apply the chat template automatically. If you use model.generate directly, you need to apply the chat template mannually.

python

from transformers import pipeline
import torch

model_id =  "LLM360/K2-Think"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "user", "content": "what is the next prime number after 2600?"},
]

outputs = pipe(
    messages,
    max_new_tokens=32768,
)
print(outputs[0]["generated_text"][-1])

Evaluation & Performance

Detailed evaluation results are reported in out Tech Report

Benchmarks (pass@1, average over 16 runs)

Domain	Benchmark	K2-Think
Math	AIME 2024	90.83
Math	AIME 2025	81.24
Math	HMMT 2025	73.75
Math	OMNI-Math-HARD	60.73
Code	LiveCodeBench v5	63.97
Science	GPQA-Diamond	71.08

Inference Speed

We deploy K2-THINK on Cerebras Wafer-Scale Engine (WSE) systems, leveraging the world’s largest processor and speculative decoding to achieve unprecedented inference speeds for our 32B reasoning system.

Platform	Throughput (tokens/sec)	Example: 32k-token response (time)
Cerebras WSE (our deployment)	~2,000	~16 s
Typical H100/H200 GPU setup	~200	~160 s

Safety Evaluation

Aggregated across four safety dimensions (Safety-4):

Aspect	Macro-Avg
High-Risk Content Refusal	0.83
Conversational Robustness	0.89
Cybersecurity & Data Protection	0.56
Jailbreak Resistance	0.72
Safety-4 Macro (avg)	0.75

Terms of Use

This model is released strictly for research and educational purposes.
By downloading, using, or interacting with this model, you agree to the following conditions:

Research Only
The model is provided as part of an academic and research project.
It is not intended for commercial deployment or production use without explicit permission.
Prohibited Uses
You may not use this model:
- For any illegal, unlawful, or harmful activities, including but not limited to generating or disseminating malicious content, engaging in fraud, violating privacy, or spreading misinformation.
- In applications that could directly cause harm, injury, or safety risks to individuals or society.
No Warranty
The model is provided “as is” without warranties of any kind.
The authors and institutions involved bear no responsibility for consequences arising from its use.
Attribution
When using or referencing the model in research, publications, or derivative works, proper citation and attribution to the authors and project must be given.
Compliance
You are responsible for ensuring that your use of the model complies with all applicable laws, regulations, and ethical guidelines in your jurisdiction.

Citation

bibtex

@misc{cheng2025k2thinkparameterefficientreasoning,
      title={K2-Think: A Parameter-Efficient Reasoning System}, 
      author={Zhoujun Cheng and Richard Fan and Shibo Hao and Taylor W. Killian and Haonan Li and Suqi Sun and Hector Ren and Alexander Moreno and Daqian Zhang and Tianjun Zhong and Yuxin Xiong and Yuanzhe Hu and Yutao Xie and Xudong Han and Yuqi Wang and Varad Pimpalkhute and Yonghao Zhuang and Aaryamonvikram Singh and Xuezhi Liang and Anze Xie and Jianshu She and Desai Fan and Chengqian Gao and Liqun Ma and Mikhail Yurochkin and John Maggs and Xuezhe Ma and Guowei He and Zhiting Hu and Zhengzhong Liu and Eric P. Xing},
      year={2025},
      eprint={2509.07604},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2509.07604}, 
}

jobs-git/K2-Think

K2-Think: A Parameter-Efficient Reasoning System

Quickstart

Transformers