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K2-V2-Instruct

Name: K2-V2-Instruct
Rating: 2.5 (298 reviews)

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K2-V2-Instruct

K2-V2 model logo

📚 Tech Report - 📝 Training Code - 🏢 Evaluation Code

🗂️ Pretraining Data: TxT360 - 🗂️ Midtraining Data: TxT360-Midas - 🗂️ SFT Data: TxT360-3efforts

K2-V2 is our most capable fully open model to date, and one of the strongest open-weight models in its class. It uses a 70B-parameter dense transformer architecture and represents the latest advancement in the LLM360 model family.

Beyond standard competencies such as factual knowledge and conversational ability, K2-V2 demonstrates strong long-context consistency, deep mathematical understanding, and robust reasoning skills. These capabilities serve as building blocks for sophisticated downstream applications, such as solving complex math problems and executing agentic workflows.

Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("llm360/k2-v2-instruct", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("llm360/k2-v2-instruct")

prompt = "Explain why the derivative of sin(x) is cos(x)."
messages = [
    {"role": "system", "content": "You are K2, a helpful assistant created by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Institute of Foundation Models (IFM)."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
    reasoning_effort="high" # Or "medium"/"low"
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Alternatively, you may serve the model using VLLM:

vllm serve LLM360/K2-V2-Instruct --tensor-parallel-size 8 --port 8000

K2-V2-Instruct uses reasoning_effort="low"|"medium"|"high" in the chat template to determine reasoning effort. 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-V2-Instruct",
    messages = [
        {"role": "system", "content": "You are K2, a helpful assistant created by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Institute of Foundation Models (IFM)."},
        {"role": "user", "content": "Explain why the derivative of sin(x) is cos(x)."}
    ],
    extra_body={
        "chat_template_kwargs": {"reasoning_effort": "high"},
    },
)

Using specific checkpoints

To use a specific checkpoint, use the revision argument:

Using transformers

model = AutoModelForCausalLM.from_pretrained("llm360/k2-v2-instruct", device_map="auto", revision="stage_1_0135000")

Using vLLM

vllm serve LLM360/K2-V2-Instruct --tensor-parallel-size 8 --port 8000 --revision stage_1_0135000

With Tool-use

To enable tool-use, load, tokenize, and generate as the examples above, but additionally define a set of tools and pass them to the chat template.

Using transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("llm360/k2-v2-instruct", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("llm360/k2-v2-instruct")

prompt = "Explain why the derivative of sin(x) is cos(x)."
messages = [
    {"role": "system", "content": "You are K2, a helpful assistant created by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Institute of Foundation Models (IFM)."},
    {"role": "user", "content": prompt}
]

# Define list of tools
tools = [
    {
        "type": "function",
        "name": "get_horoscope",
        "description": "Get today's horoscope for an astrological sign.",
        "parameters": {
            "type": "object",
            "properties": {
                "sign": {
                    "type": "string",
                    "description": "An astrological sign like Taurus or Aquarius",
                },
            },
            "required": ["sign"],
        },
    },
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
    reasoning_effort="high" # Or "medium"/"low"
    tools=tools
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Using vLLM

Serve the model with tool-use enabled and parser set.

vllm serve LLM360/K2-V2-Instruct --enable-auto-tool-choice --tool-call-parser hermes --tensor-parallel-size 8 --port 8000

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="key"
)

# Define list of tools
tools = [
    {
        "type": "function",
        "name": "get_horoscope",
        "description": "Get today's horoscope for an astrological sign.",
        "parameters": {
            "type": "object",
            "properties": {
                "sign": {
                    "type": "string",
                    "description": "An astrological sign like Taurus or Aquarius",
                },
            },
            "required": ["sign"],
        },
    },
]

completion = client.chat.completions.create(
    model="LLM360/K2-V2-Instruct",
    messages = [
        {"role": "system", "content": "You are K2, a helpful assistant created by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Institute of Foundation Models (IFM)."},
        {"role": "user", "content": "Explain why the derivative of sin(x) is cos(x)."}
    ],
    extra_body={
        "chat_template_kwargs": {"reasoning_effort": "high"},
    },
    tools=tools,
)

Evaluation Summary

Below we report performance across general, reasoning, mathematical, and coding benchmarks. Scores for K2-V2 checkpoints (base → mid-4) demonstrate the impact of staged mid-training on reasoning quality.

Task / Model	base	mid-1	mid-2	mid-3	mid-4	Qwen2.5-72B	Llama3.0-70B	Llama3.1-70B	Olmo3-32B
General Tasks
MMLU	74.3	74.4	73.5	75.0	75.2	86.1	79.5	79.3	75.2
MMLU-Pro	43.7	46.8	48.1	59.8	57.0	58.1	52.8	53.8	49.6
BBH	68.4	79.8	81.1	82.2	83.2	86.3	82.2	82.1	77.6
HELLASWAG	87.8	86.9	86.6	86.6	86.0	87.6	88.0	85.0	84.8
WINOGRANDE	82.6	83.7	83.7	83.7	83.0	83.9	85.3	79.8	90.3
PIQA	84.2	84.0	83.3	82.9	83.1	83.5	84.6	84.3	85.6
TRUTHFULQA	54.0	54.9	55.1	55.8	53.9	60.5	45.6	49.7	54.9
Math & STEM Tasks
GPQA-DIAMOND	26.3	31.3	27.8	43.9	55.1	34.9	21.2	27.3	30.3
GSM8K	68.0	76.4	82.1	93.6	92.5	91.2	83.2	81.1	80.5
MATH	27.8	38.2	41.1	94.7	91.4	58.5	41.9	41.6	43.4
AIME 2025	0.0	17.6	25.1	53.2	46.9	1.7	0.1	0.2	14.7
ARC-CHALLENGE	64.9	66.4	66.4	66.0	66.3	72.4	69.2	64.9	65.4
Coding Tasks
MBPP	57.6	57.8	58.2	59.8	61.8	75.4	69.2	64.4	60.2
HUMANEVAL	50.0	51.2	53.7	54.3	54.3	54.3	42.1	50.6	36.0
Logic Puzzles
COUNTDOWN	1.3	53.3	53.1	35.9	75.6	6.0	1.0	0.5	23.2
KK-4 PEOPLE	4.8	44.9	68.0	64.5	92.9	26.1	4.2	7.6	42.4
KK-8 PEOPLE	0.5	23.2	41.3	51.6	82.8	5.7	1.1	1.3	13.0
ORDER-15 ITEMS	4.7	30.7	47.2	55.8	87.6	37.0	3.5	4.5	25.0
ORDER-30 ITEMS	0.0	0.3	3.0	34.1	40.3	0.7	0.2	0.1	0.6
Instruction Following
IFEVAL	17.4	26.2	28.5	34.5	26.7	40.3	15.1	17.4	13.2
Arabic
MMLU-Arabic	65.4	66.1	64.5	66.6	65.5	74.1	65.0	66.8	47.8

Below we report the evaluation results for K2-V2 after supervised fine-tuning (SFT). These variants correspond to three levels of reasoning effort (Low < Medium < High).

Metric / Model	K2 Low _{Dense · 70B}	K2 Medium _{Dense · 70B}	K2 High _{Dense · 70B}	Olmo3 Think SFT _{Dense · 32B · No RL}	Olmo3 Think _{Dense · 32B · RL}	GLM-4.5 Air _{MoE · 106B A12B}	MiniMax-M2 _{MoE · 230B A10B}	Qwen3 235B _{MoE · 235B A22B · Reasoning}	Qwen 2.5 72B _{Dense · 72B}
LongBench V2	40.7	41.3	42.6	42.8	47.1	49.4	55.8	60.9	47.2
AIME25	27.3	62.0	80.2	68.3	73.3	81.3	75.8	88.8	15.2
HMMT25	19.0	45.6	71.4	43.3	50.83	73.3	63.5	84.2	9.79
GSM8K	92.4	92.0	94.8	96.1	95.7	96.1	95.4	93.5	85.8
Minerva	85.0	90.6	94.5	96.9	97.3	94.9	85.3	98.0	82.1
GPQA-D	48.5	60.6	69.3	58.0	59.8	75.3	76.2	80.7	50.5
MBPP	71.0	75.8	84.8	87.6	91.6	82.8	83.8	96.2	80.0
HumanEval	82.3	91.5	91.5	96.3	96.3	97.6	89.6	94.5	85.4
LCBv6	39.9	51.3	67.0	67.9	67.6	67.8	79.2	72.8	36.7
IFEVAL	73.2	82.7	90.0	80.1	88.7	88.7	89.6	88.7	85.7

Please refer to our Tech Report for detailed evaluation results.

Datasets & Mixtures

SFT Mix

TxT360-3efforts: curated instruction + mixed-difficulty reasoning traces
Tool-calling demonstrations
Small but high-value corpus to showcase model potential

All mixtures, filtering rules, and data sources are fully released for reproducibility.

Please refer to our Tech Report for detailed datasets and mixtures information.

Model Description

Model type: K2-V2 follows a standard decoder-only transformer with grouped-query attention and RMSNorm.
Training stage: Pre-training & Post-training
Language(s) (NLP): English
License: Apache 2.0

Model Hyperparameter	Value
Total Parameters	70B
Hidden Size	8,192
Intermediate Size (FFN)	28,672
Number of Attention Heads	64
Number of Layers	80
RMSNorm ɛ	1e-5
Pre-training Seq Length	8,192
Post-training Seq Length	524,288
Vocab Size	250,000

Citation

If you use K2-V2-Instruct in your research, please cite the following:

@misc{k2team2025k2v2360openreasoningenhancedllm,
      title={K2-V2: A 360-Open, Reasoning-Enhanced LLM}, 
      author={K2 Team and Zhengzhong Liu and Liping Tang and Linghao Jin and Haonan Li and Nikhil Ranjan and Desai Fan and Shaurya Rohatgi and Richard Fan and Omkar Pangarkar and Huijuan Wang and Zhoujun Cheng and Suqi Sun and Seungwook Han and Bowen Tan and Gurpreet Gosal and Xudong Han and Varad Pimpalkhute and Shibo Hao and Ming Shan Hee and Joel Hestness and Haolong Jia and Liqun Ma and Aaryamonvikram Singh and Daria Soboleva and Natalia Vassilieva and Renxi Wang and Yingquan Wu and Yuekai Sun and Taylor Killian and Alexander Moreno and John Maggs and Hector Ren and Guowei He and Hongyi Wang and Xuezhe Ma and Yuqi Wang and Mikhail Yurochkin and Eric P. Xing},
      year={2025},
      eprint={2512.06201},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2512.06201}, 
}

IFM/K2-V2-Instruct