LGAI-EXAONE/K-EXAONE-2.0-750B-A37B · Hugging Face

Source: https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#introductionIntroduction

We introduceK-EXAONE 2.0, a frontier-scale multilingual language model developed by LG AI Research. K-EXAONE 2.0 was scaled to more than three times the size of its predecessor through upcycling, followed by continual pretraining, difficulty-focused mid-training, and post-training. K-EXAONE 2.0 is broadly competitive with leading open-weight models, demonstrating substantial improvements over its predecessor and achieving particularly strong results in long-context retrieval and safety.

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#highlightsHighlights

• Frontier-Class ScaleTo build a large-scale foundation model with frontier-level intelligence, we upcycled theK-EXAONEmodel by expanding both its depth and width, resulting in a more favorable scaling curve. During this process, we found that clamping after two SwiGLU branches effectively mitigates the exploding activations in deeper layers, improving both training and inference stability.
• Advanced Reasoning & Agentic IntelligenceIn response to the growth of agentic AI, we focused on expanding the model’s capabilities in reasoning, agentic workflows, and long-context management. Through careful calibration of the training data and recipes, K-EXAONE 2.0 achieves consistent improvements in agentic coding and long-context understanding, with strong performance on long-context retrieval and safety.
• Production-Ready InferenceWe support two speculative decoding methods to accelerate inference: MTP (Multi-Token Prediction) and DSpark. Both methods can speed up model generation by approximately 3–5×, reducing latency for long-horizon workloads such as agentic tasks.
• Multilinguality & OpennessWe expanded multilingual coverage from six to ten languages: Korean, English, Spanish, German, Japanese, Vietnamese, French, Italian, Polish, and Portuguese. We also release K-EXAONE 2.0 under theApache license 2.0so that the broader AI ecosystem can inspect, deploy, and build upon it.

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#model-configurationsModel Configurations

Number of Parameters750BActive Parameters37BHidden Dimension6,144Intermediate Size18,432Number of Layers78 (2 heading Dense + 76 Sparse) Main layers 1 MTP layers Attention1 x Global (NoPE) 1 x 4096 SWA 19 x [3 x 128 SWA + 1 x Global] BlocksAttention Heads64 Q-heads / 8 KV-headsHead Dimension128Number of Experts1 Shared Expert 256 Total Experts 8 Activated ExpertsExpert Dimension2,048Vocab Size153,600Context Length262,144Knowledge Cutoff2025 2Q

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#evaluation-resultsEvaluation Results

The following table shows the benchmark results for the K-EXAONE 2.0 BF16 model. Detailed evaluation results and configurations can be found in ourtechnical report.

K-EXAONE 2.0K-EXAONEQwen3.5GLM-5.1DSV4 Pro (max)ArchitectureMoEMoEMoEMoEMoETotal Params750B236B397B754B1.6TActive Params37B23B17B40B49BWorld KnowledgeMMLU-Pro83.583.889.886.087.5GPQA-Diamond82.279.188.486.290.1Humanity’s Last Exam18.313.628.731.037.7MathAIME 202692.392.291.395.395.2HMMT Feb 202678.480.784.682.695.2IMO Answer78.676.380.983.889.8Coding / Agentic CodingSciCode37.435.642.043.850.0SWE Bench Verified68.249.476.473.680.6Terminal-Bench 2.143.830.351.361.864.0Agentic Tool Useτ3-Banking14.214.213.411.525.8Claw-Eval77.770.379.784.482.7Instruction FollowingIFEval92.489.792.693.994.0IFBench72.667.376.576.376.5Long Context UnderstandingOpenAI-MRCR94.452.393.071.592.9AA-LCR56.253.565.762.366.3Ko-LongBench89.686.891.383.691.4KoreanKMMLU-Pro69.167.377.475.880.5Click84.283.988.988.791.6HRM8K-KSM91.191.991.289.494.3MultilingualityMMMLU86.686.290.689.789.6GlobalMMLU-Lite86.686.992.190.792.0PolyMath71.357.473.373.880.9SafetyKGC-Safety99.896.192.069.382.8ROK-Fortress89.560.986.173.247.6## https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#quickstartQuickstart

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#serving-k-exaone-20Serving K-EXAONE 2.0

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#sglangSGLang

Install SGLang fromour forkas follows:

uv venv
source .venv/bin/activate
uv pip install git+https://github.com/lkm2835/sglang@add-k-exaone2
uv pip install git+https://github.com/nuxlear/transformers@add-k-exaone2

The following script shows how to serve K-EXAONE 2.0 with SGLang on two nodes of 8 x NVIDIA H200 GPUs. Before starting the server, set$HEAD\_ADDRto the IP address of the head (rank-0) node including the port number, and set$NODE\_RANKto the number index of the node.

Run the one of the following scripts on every node, according to your purpose.

• Low latency sglang serve \ --model-path LGAI-EXAONE/K-EXAONE-2.0-750B-A37B \ --served-model-name K-EXAONE-2.0-750B-A37B \ --tp 16 \ --dist-init-addr $HEAD_ADDR \ --nnodes 2 \ --node-rank $NODE_RANK \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder \ --host 0.0.0.0 \ --port 8000 \ --max-running-requests 128 \ --speculative-algo EAGLE \ --speculative-num-steps 4 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 5 \ --mem-fraction-static 0.875 \ --swa-full-tokens-ratio 0.3
• High throughput sglang serve \ --model-path LGAI-EXAONE/K-EXAONE-2.0-750B-A37B \ --served-model-name K-EXAONE-2.0-750B-A37B \ --tp 8 \ --dp 2 \ --enable-dp-attention \ --dist-init-addr $HEAD_ADDR \ --nnodes 2 \ --node-rank $NODE_RANK \ --reasoning-parser qwen3 \ --tool-call-parser qwen3_coder \ --host 0.0.0.0 \ --port 8000 \ --max-running-requests 192 \ --cuda-graph-max-bs 96 \ --speculative-algo EAGLE \ --speculative-num-steps 4 \ --speculative-eagle-topk 1 \ --speculative-num-draft-tokens 5 \ --mem-fraction-static 0.875 \ --swa-full-tokens-ratio 0.3

If you are using NVIDIA B200 GPUs, please add the\-\-disable\-prefill\-cuda\-graphoption to prevent issues with model generation collapse. We will update this note once these issues have been resolved.

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#vllmvLLM

You should install the vLLM library fromour forkas below:

uv venv
source .venv/bin/activate
uv pip install git+https://github.com/lkm2835/vllm@add-k-exaone2 --torch-backend auto
uv pip install git+https://github.com/nuxlear/transformers@add-k-exaone2

The following script shows how to serve K-EXAONE 2.0 with vLLM on two nodes of 8 x NVIDIA H200 GPUs. Before starting the server, set$HEAD\_IPto the IP address of the head (rank-0) node, and set$NODE\_RANKto the number index of the node.

Run the below script on every server.

HEADLESS_ARG=''
if [ \"\$NODE_RANK\" -ne 0 ]; then
    HEADLESS_ARG='--headless'
fi

exec vllm serve LGAI-EXAONE/K-EXAONE-2.0-750B-A37B \
    --served-model-name K-EXAONE-2.0-750B-A37B \
    --trust-remote-code \
    --tensor-parallel-size 16 \
    --distributed-executor-backend mp \
    --nnodes 2 \
    --node-rank $NODE_RANK \
    --master-addr $HEAD_IP \
    --master-addr 30000 \
    --gpu-memory-utilization 0.9 \
    --max-num-seqs 256 \
    --reasoning-parser qwen3 \
    --enable-auto-tool-choice \
    --tool-call-parser qwen3_xml \
    --host 0.0.0.0 \
    --port 8000 \
    --speculative_config '{
        "method": "mtp", 
        "num_speculative_tokens": 4
    }' \
    $HEADLESS_ARG

Currently, serving K-EXAONE 2.0 with DSpark is not supported on vLLM. We will update this note once these issues have been resolved.

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#using-k-exaone-20Using K-EXAONE 2.0

To achieve the expected performance, we recommend using the following configurations: - We recommend usingtemperature=1\.0andtop\_p=0\.95for better output quality in most cases. - K-EXAONE 2.0 usesenable\_thinking=Trueby default. Thus, you need to setenable\_thinking=Falseto use non-reasoning mode. - We recommend usingpreserve\_thinking=Truein long-running tasks, such as agentic use or deep research.

Once K-EXAONE 2.0 is running on an inference engine, you can access it using the OpenAI Python SDK.

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#reasoning-modeReasoning mode

For tasks that require high accuracy, you can use the K-EXAONE 2.0 model in reasoning mode. The K-EXAONE 2.0 model supports thepreserve\_thinkingoption, which allows it to track previousreasoning\_contentacross subsequent conversations. For agentic workflows, it is recommended to usepreserve\_thinking=True.

from openai import OpenAI

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

messages = [
    {
        "role": "user",
        "content": "Implement fibonacci with python code.",
    }
]

response = client.chat.completions.create(
    model="LGAI-EXAONE/K-EXAONE-2.0-750B-A37B",
    messages=messages,
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": True,  # default: True
            "preserve_thinking": True,  # default: False
        }
    }, 
)
print(response)

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#non-reasoning-modeNon-reasoning mode

For tasks where latency matters more than accuracy, you can run the K-EXAONE 2.0 model in non-reasoning mode.

from openai import OpenAI

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

messages = [
    {
        "role": "user",
        "content": "Explain how useful you are.",
    }
]

response = client.chat.completions.create(
    model="LGAI-EXAONE/K-EXAONE-2.0-750B-A37B",
    messages=messages,
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": False,  # default: True
            "preserve_thinking": False,  # default: False
        }
    }, 
)
print(response)

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#tool-callingTool calling

For your AI-powered agent, you can leverage K-EXAONE 2.0’s tool calling capability.

from openai import OpenAI

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

tools = [
    {
        "type": "function",
        "function": {
            "name": "roll_dice",
            "description": "Roll the dice with the number 1 to N. User can select the number N.",
            "parameters": {
                "type": "object",
                "properties": {
                    "max_num": {
                        "type": "integer",
                        "description": "The maximum number on the dice."
                    }
                },
                "required": ["max_num"]
            },
        },
    }
]

messages = [
    {
        "role": "user",
        "content": "Roll a D20 twice and sum the results."
    }
]

response = client.chat.completions.create(
    model="LGAI-EXAONE/K-EXAONE-2.0-750B-A37B",
    messages=messages,
    tools=tools,
    max_tokens=32768,
    temperature=1.0,
    top_p=0.95,
    extra_body={
        "chat_template_kwargs": {
            "enable_thinking": True,  # default: True
            "preserve_thinking": True,  # default: False
        }
    },
)

print(response)

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#agentic-useAgentic Use

You can leverage K-EXAONE 2.0’s agentic capabilities by integrating it with agent frameworks and harnesses.

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#opencodeOpenCode

To use the deployed K-EXAONE 2.0 model, you may need to update youropencode\.jsonfile. Below is an example JSON configuration for setting up a custom server as your model provider.

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "local": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Local OpenAI-compatible server",
      "options": {
        "baseURL": "http://localhost:8000/v1",
        "extraBody": {
          "chat_template_kwargs": {
            "enable_thinking": true,
            "preserve_thinking": true
          }
        }
      },
      "models": {
        "K-EXAONE-2": {
          "name": "K-EXAONE 2.0",
          "limit": {
            "context": 262144,
            "output": 32768
          }
        }
      }
    }
  }
}

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#limitationLimitation

K-EXAONE 2.0 language models, like all existing language models, have certain limitations and may occasionally generate inappropriate responses. The language model generates responses based on the output probability of tokens, and it is determined during learning from training data. While we make every effort to exclude personal, harmful, and biased information from the training data, some problematic content may still be included, potentially leading to undesirable responses. Please note that the text generated by K-EXAONE 2.0 language models does not reflect the views of LG AI Research.

• Inappropriate answers may be generated, which contain personal, harmful or other inappropriate information.
• Biased responses may be generated, which are associated with age, gender, race, and so on.
• The generated responses rely heavily on statistics from the training data, which can result in the generation of semantically or syntactically incorrect sentences.
• Since the models do not reflect the latest information, the responses may be false or contradictory.

LG AI Research strives to reduce potential risks that may arise from K-EXAONE 2.0 language models. Users are not allowed to engage in any malicious activities (e.g., keying in illegal information) that may induce the creation of inappropriate outputs violating LG AI’s ethical principles when using K-EXAONE 2.0 language models.

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#licenseLicense

The model is licensed underApache License 2.0.

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#citationCitation

@article{k-exaone-2.0,
  title={K-EXAONE 2.0 Technical Report},
  author={{LG AI Research}},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2026}
}

https://huggingface.co/LGAI-EXAONE/K-EXAONE-2.0-750B-A37B#contactContact

LG AI Research Technical Support:[email protected]