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LGAI-EXAONE
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EXAONE-4.0-32B-GGUF

Text Generation
Transformers
GGUF
English
Korean
Spanish
lg-ai
exaone
exaone-4.0
conversational
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EXAONE-4.0-32B-GGUF

Introduction

We introduce EXAONE 4.0, which integrates a Non-reasoning mode and Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean.

The EXAONE 4.0 model series consists of two sizes: a mid-size 32B model optimized for high performance, and a small-size 1.2B model designed for on-device applications.

In the EXAONE 4.0 architecture, we apply new architectural changes compared to previous EXAONE models as below:

  1. Hybrid Attention: For the 32B model, we adopt hybrid attention scheme, which combines Local attention (sliding window attention) with Global attention (full attention) in a 3:1 ratio. We do not use RoPE (Rotary Positional Embedding) for global attention for better global context understanding.
  2. QK-Reorder-Norm: We adopt the Post-LN (LayerNorm) scheme for transformer blocks instead of Pre-LN, and we add RMS normalization right after the Q and K projection. It helps yield better performance on downstream tasks despite consuming more computation.

For more details, please refer to our technical report, blog, and GitHub.

Model Configuration

  • Number of Parameters (without embeddings): [[num_params_wo_embeddings]]
  • Number of Layers: [[num_layers]]
  • Number of Attention Heads: [[num_heads]]
  • Vocab Size: 102,400
  • Context Length: [[context_length]] tokens [[quantization]]

Quickstart

llama.cpp

You can run EXAONE models locally using llama.cpp by following these steps:

  1. Install the latest version of llama.cpp, by cloning the our PR and building from source. Please refer to the official documentation about building from source.
git clone --single-branch -b add-exaone4 https://github.com/lgai-exaone/llama.cpp.git

  1. Download the EXAONE 4.0 model weights in GGUF format.

    huggingface-cli download LGAI-EXAONE/EXAONE-4.0-32B-GGUF-GGUF \
    --include "EXAONE-4.0-32B-GGUF-Q4_K_M.gguf" \
    --local-dir .

    When you use GGUF model split into multiple files, you should merge them into a single file before running the model.

    1. First, download the GGUF model weights.

      huggingface-cli download LGAI-EXAONE/EXAONE-4.0-32B-GGUF \
    --include "EXAONE-4.0-32B-BF16*.gguf" \
    --local-dir .

    2. Merge the split files into a single file.

      llama-gguf-split --merge \
    ./EXAONE-4.0-32B-BF16-00001-of-00002.gguf \
    ./EXAONE-4.0-32B-BF16.gguf
Generation with `llama-cli`

  1. Apply chat template using transformers.

    This process is necessary to avoid issues with current EXAONE modeling code in llama.cpp. This is work in progress at our PR. We will update this once these issues are solved.

    from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "LGAI-EXAONE/EXAONE-4.0-32B-GGUF"
tokenizer = AutoTokenizer.from_pretrained(model_name)

messages = [
    {"role": "user", "content": "Let's work together on local system!"}
]
input_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

print(repr(input_text))
with open("inputs.txt", "w") as f:
    f.write(input_text)

  2. Generate result with greedy decoding.

    llama-cli -m EXAONE-4.0-32B-GGUF-Q4_K_M.gguf \
    -fa -ngl 64 \
    --temp 0.0 --top-k 1 \
    -f inputs.txt -no-cnv
OpenAI compatible server with `llama-server`

  1. Run llama-server with EXAONE 4.0 Jinja template.

    llama-server -m EXAONE-4.0-32B-Q4_K_M.gguf \
    -c 131072 -fa -ngl 64 \
    --temp 0.6 --top-p 0.95 \
    --jinja --chat-template-format chat_template_simple.jinja \
    --host 0.0.0.0 --port 8820 \
    -a EXAONE-4.0-32B-Q4_K_M

  2. Use OpenAI chat completion to test the GGUF model.

    The implementation of llama.cpp would not be optimized for some usage including reasoning mode or agentic use. 

    curl -X POST http://localhost:8820/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "EXAONE-4.0-32B-Q4_K_M",
        "messages": [
            {"role": "user", "content": "Let'\''s work together on server!"}
        ],
        "max_tokens": 1024,
        "temperature": 0.6,
        "top_p": 0.95
    }'

Performance

The following tables show the evaluation results of each model, with reasoning and non-reasoning mode. The evaluation details can be found in the technical report.

  • ✅ denotes the model has a hybrid reasoning capability, evaluated by selecting reasoning / non-reasoning on the purpose.
  • To assess Korean practical and professional knowledge, we adopt both the KMMLU-Redux and KMMLU-Pro benchmarks. Both datasets are publicly released!
  • The evaluation results are based on the original model, not quantized model.

32B Reasoning Mode

EXAONE 4.0 32B Phi 4 reasoning-plus Magistral Small-2506 Qwen 3 32B Qwen 3 235B DeepSeek R1-0528
Model Size 32.0B 14.7B 23.6B 32.8B 235B 671B
Hybrid Reasoning ✅ ✅ ✅
World Knowledge
MMLU-Redux 92.3 90.8 86.8 90.9 92.7 93.4
MMLU-Pro 81.8 76.0 73.4 80.0 83.0 85.0
GPQA-Diamond 75.4 68.9 68.2 68.4 71.1 81.0
Math/Coding
AIME 2025 85.3 78.0 62.8 72.9 81.5 87.5
HMMT Feb 2025 72.9 53.6 43.5 50.4 62.5 79.4
LiveCodeBench v5 72.6 51.7 55.8 65.7 70.7 75.2
LiveCodeBench v6 66.7 47.1 47.4 60.1 58.9 70.3
Instruction Following
IFEval 83.7 84.9 37.9 85.0 83.4 80.8
Multi-IF (EN) 73.5 56.1 27.4 73.4 73.4 72.0
Agentic Tool Use
BFCL-v3 63.9 N/A 40.4 70.3 70.8 64.7
Tau-bench (Airline) 51.5 N/A 38.5 34.5 37.5 53.5
Tau-bench (Retail) 62.8 N/A 10.2 55.2 58.3 63.9
Multilinguality
KMMLU-Pro 67.7 55.8 51.5 61.4 68.1 71.7
KMMLU-Redux 72.7 62.7 54.6 67.5 74.5 77.0
KSM 87.6 79.8 71.9 82.8 86.2 86.7
MMMLU (ES) 85.6 84.3 68.9 82.8 86.7 88.2
MATH500 (ES) 95.8 94.2 83.5 94.3 95.1 96.0

32B Non-Reasoning Mode

EXAONE 4.0 32B Phi 4 Mistral-Small-2506 Gemma 3 27B Qwen3 32B Qwen3 235B Llama-4-Maverick DeepSeek V3-0324
Model Size 32.0B 14.7B 24.0B 27.4B 32.8B 235B 402B 671B
Hybrid Reasoning ✅ ✅ ✅
World Knowledge
MMLU-Redux 89.8 88.3 85.9 85.0 85.7 89.2 92.3 92.3
MMLU-Pro 77.6 70.4 69.1 67.5 74.4 77.4 80.5 81.2
GPQA-Diamond 63.7 56.1 46.1 42.4 54.6 62.9 69.8 68.4
Math/Coding
AIME 2025 35.9 17.8 30.2 23.8 20.2 24.7 18.0 50.0
HMMT Feb 2025 21.8 4.0 16.9 10.3 9.8 11.9 7.3 29.2
LiveCodeBench v5 43.3 24.6 25.8 27.5 31.3 35.3 43.4 46.7
LiveCodeBench v6 43.1 27.4 26.9 29.7 28.0 31.4 32.7 44.0
Instruction Following
IFEval 84.8 63.0 77.8 82.6 83.2 83.2 85.4 81.2
Multi-IF (EN) 71.6 47.7 63.2 72.1 71.9 72.5 77.9 68.3
Long Context
HELMET 58.3 N/A 61.9 58.3 54.5 63.3 13.7 N/A
RULER 88.2 N/A 71.8 66.0 85.6 90.6 2.9 N/A
LongBench v1 48.1 N/A 51.5 51.5 44.2 45.3 34.7 N/A
Agentic Tool Use
BFCL-v3 65.2 N/A 57.7 N/A 63.0 68.0 52.9 63.8
Tau-Bench (Airline) 25.5 N/A 36.1 N/A 16.0 27.0 38.0 40.5
Tau-Bench (Retail) 55.9 N/A 35.5 N/A 47.6 56.5 6.5 68.5
Multilinguality
KMMLU-Pro 60.0 44.8 51.0 50.7 58.3 64.4 68.8 67.3
KMMLU-Redux 64.8 50.1 53.6 53.3 64.4 71.7 76.9 72.2
KSM 59.8 29.1 35.5 36.1 41.3 46.6 40.6 63.5
Ko-LongBench 76.9 N/A 55.4 72.0 73.9 74.6 65.6 N/A
MMMLU (ES) 80.6 81.2 78.4 78.7 82.1 83.7 86.9 86.7
MATH500 (ES) 87.3 78.2 83.4 86.8 84.7 87.2 78.7 89.2
WMT24++ (ES) 90.7 89.3 92.2 93.1 91.4 92.9 92.7 94.3

1.2B Reasoning Mode

EXAONE 4.0 1.2B EXAONE Deep 2.4B Qwen 3 0.6B Qwen 3 1.7B SmolLM3 3B
Model Size 1.28B 2.41B 596M 1.72B 3.08B
Hybrid Reasoning ✅ ✅ ✅ ✅
World Knowledge
MMLU-Redux 71.5 68.9 55.6 73.9 74.8
MMLU-Pro 59.3 56.4 38.3 57.7 57.8
GPQA-Diamond 52.0 54.3 27.9 40.1 41.7
Math/Coding
AIME 2025 45.2 47.9 15.1 36.8 36.7
HMMT Feb 2025 34.0 27.3 7.0 21.8 26.0
LiveCodeBench v5 44.6 47.2 12.3 33.2 27.6
LiveCodeBench v6 45.3 43.1 16.4 29.9 29.1
Instruction Following
IFEval 67.8 71.0 59.2 72.5 71.2
Multi-IF (EN) 53.9 54.5 37.5 53.5 47.5
Agentic Tool Use
BFCL-v3 52.9 N/A 46.4 56.6 37.1
Tau-Bench (Airline) 20.5 N/A 22.0 31.0 37.0
Tau-Bench (Retail) 28.1 N/A 3.3 6.5 5.4
Multilinguality
KMMLU-Pro 42.7 24.6 21.6 38.3 30.5
KMMLU-Redux 46.9 25.0 24.5 38.0 33.7
KSM 60.6 60.9 22.8 52.9 49.7
MMMLU (ES) 62.4 51.4 48.8 64.5 64.7
MATH500 (ES) 88.8 84.5 70.6 87.9 87.5

1.2B Non-Reasoning Mode

EXAONE 4.0 1.2B Qwen 3 0.6B Gemma 3 1B Qwen 3 1.7B SmolLM3 3B
Model Size 1.28B 596M 1.00B 1.72B 3.08B
Hybrid Reasoning ✅ ✅ ✅ ✅
World Knowledge
MMLU-Redux 66.9 44.6 40.9 63.4 65.0
MMLU-Pro 52.0 26.6 14.7 43.7 43.6
GPQA-Diamond 40.1 22.9 19.2 28.6 35.7
Math/Coding
AIME 2025 23.5 2.6 2.1 9.8 9.3
HMMT Feb 2025 13.0 1.0 1.5 5.1 4.7
LiveCodeBench v5 26.4 3.6 1.8 11.6 11.4
LiveCodeBench v6 30.1 6.9 2.3 16.6 20.6
Instruction Following
IFEval 74.7 54.5 80.2 68.2 76.7
Multi-IF (EN) 62.1 37.5 32.5 51.0 51.9
Long Context
HELMET 41.2 21.1 N/A 33.8 38.6
RULER 77.4 55.1 N/A 65.9 66.3
LongBench v1 36.9 32.4 N/A 41.9 39.9
Agentic Tool Use
BFCL-v3 55.7 44.1 N/A 52.2 47.3
Tau-Bench (Airline) 10.0 31.5 N/A 13.5 38.0
Tau-Bench (Retail) 21.7 5.7 N/A 4.6 6.7
Multilinguality
KMMLU-Pro 37.5 24.6 9.7 29.5 27.6
KMMLU-Redux 40.4 22.8 19.4 29.8 26.4
KSM 26.3 0.1 22.8 16.3 16.1
Ko-LongBench 69.8 16.4 N/A 57.1 15.7
MMMLU (ES) 54.6 39.5 35.9 54.3 55.1
MATH500 (ES) 71.2 38.5 41.2 66.0 62.4
WMT24++ (ES) 65.9 58.2 76.9 76.7 84.0

Usage Guideline

To achieve the expected performance, we recommend using the following configurations:

  • For non-reasoning mode, we recommend using a lower temperature value such as temperature<0.6 for better performance.
  • For reasoning mode (using <think> block), we recommend using temperature=0.6 and top_p=0.95.
    • If you suffer from the model degeneration, we recommend using presence_penalty=1.5.
  • For Korean general conversation with 1.2B model, we suggest to use temperature=0.1 to avoid code switching.

Limitation

The EXAONE language model has 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 have made 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 EXAONE language model 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 model does not reflect the latest information, the responses may be false or contradictory.

LG AI Research strives to reduce potential risks that may arise from EXAONE 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 EXAONE language models.

License

The model is licensed under EXAONE AI Model License Agreement 1.2 - NC

The main difference from the older version is as below:

  • We removed the claim of model output ownership from the license.
  • We restrict the model use against the development of models that compete with EXAONE.
  • We allow the model to be used for educational purposes, not just research.

Citation 

@article{exaone-4.0,
  title={EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modes},
  author={{LG AI Research}},
  journal={arXiv preprint arXiv:2507.11407},
  year={2025}
}

Contact

LG AI Research Technical Support: [email protected]

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