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IFM/K2-Think-V2
K2-Think-V2 is a text generation model from IFM. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
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From the Hugging Face model README
π Blog - π Code - π’ Project Page
<center><img src="banner.png" alt="k2-think-banner"/></center> <br>K2 Think V2 is a 70 billion parameter open-weights general reasoning model with strong performance in competitive mathematical problem solving built on-top of K2-V2-Instruct, comprising a fully sovereign reasoning model.
We use the following serving configurations:
| Setting | Value |
|---|---|
| Temperature | 1.0 |
| Top-p | 1.0 |
| Top-k | -1 |
| Context Length | 131072 |
| Context Length Extension | 2x using YaRN |
| Chat Template | Default provided in chat_template.jinja |
The provided chat template sets the reasoning effort to high
To serve on VLLM:
vllm serve LLM360/K2-Think-V2 --tensor-parallel-size 8 --port 8000
You can use K2 Think V2 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.
The chat template is directly inherited from K2-V2-Instruct, with the default reasoning_effort set to "high". The other levels of reasoning effort ("low" and "medium") are still available but have not been tested or evaluated. As such, the model's behavior under such settings is not assured to maintain reported performance.
from transformers import pipeline
import torch
model_id = "LLM360/K2-Think-V2"
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=131072,
)
print(outputs[0]["generated_text"][-1])
If you cannot use tokenizer.apply_chat_template, you may also pass in these arguments using extra_body and chat_template_kwargs:
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:8000/v1",
api_key="key"
)
completion = client.chat.completions.create(
model="LLM360/K2-Think-V2",
messages = [
{"role": "system", "content": "You are K2-Think, a helpful assistant created by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Institute of Foundation Models (IFM)."},
{"role": "user", "content": "Solve the 24 game [2, 3, 5, 6]"}
],
extra_body={
"chat_template_kwargs": {"reasoning_effort": "high"},
},
)
A more complete summary of evaluation results are reported in our Blog
| Domain | Benchmark | K2 Think V2 |
|---|---|---|
| Math | AIME 2025 | 90.42 |
| Math | HMMT 2025 | 84.79 |
| Code | SciCode | 33.00 |
| Science | GPQA-Diamond | 72.98 |
| Science | Humanity's Last Exam | 9.5 |
Aggregated across four safety dimensions (Safety-4):
K2 Think V2 establishes a robust safety baseline while effectively resolving the "alignment tax" of previous K2 Think releases. Despite strong overall safety performance, there are still opportunities to improve the model with regard to handling sensitive personal information.
| Safety Surface | Macro-Avg | Risk Level |
|---|---|---|
| Content & Public Safety | 98.20 | Low |
| Truthfulness & Reliability | 97.98 | Low |
| Societal Alignment | 97.25 | Low |
| Data & Infrastructure | 83.00 | Critical |
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
User Responsibility
No Warranty
If you use K2 Think V2 in your research, please use the following citation:
@misc{k2think2026k2think0126,
title={K2 {T}hink {V}2: A {F}ully-{S}overeign {R}easoning {M}odel},
author={K2 Think Team and Taylor W. Killian and Varad Pimpalkhute and Richard Fan and Haonan Li and Chengqian Gao and Ming Shan Hee and Xudong Han and John Maggs and Guowei He and Zhengzhong Liu and Eric P. Xing},
year={2026},
url={https://mbzuai.ac.ae/news/k2-think-v2-a-fully-sovereign-reasoning-model},
}