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 Cloud Service 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

We have employed various techniques to reduce bias, harmful outputs, and other risks in the model. While these efforts help improve safety and reliability, the model, like all Large Language Models, may still generate inaccurate, misleading, biased, or otherwise undesirable content. By downloading, using, or interacting with this model, you acknowledge these limitations and agree to the following:

Prohibited Uses
- You may not use this model for any illegal, unlawful, or harmful activities, including but not limited to fraud, abuse, harassment, privacy violations, or the creation/dissemination of malicious content.
User Responsibility
- You are solely responsible for how you use the model and for any outcomes that result from its use.
- The authors and institutions involved in releasing this model do not accept liability for any consequences arising from its use.
No Warranty
- The model is provided “as is” without any warranties or guarantees.

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}, 
}

FilledVaccum/K2-Think-Quantized

LLM360/K2-Think (Quantized)

Description

Quantization Details

📄 Original Model Information

K2-Think: A Parameter-Efficient Reasoning System

Quickstart

Transformers