--- base_model: LGAI-EXAONE/EXAONE-4.0-32B base_model_relation: quantized license: other license_name: exaone license_link: LICENSE language: - en - ko - es tags: - lg-ai - exaone - exaone-4.0 pipeline_tag: text-generation library_name: transformers ---

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# EXAONE-4.0-32B-GPTQ ## 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](https://github.com/LG-AI-EXAONE/EXAONE-3.5) and the advanced reasoning abilities of [EXAONE Deep](https://github.com/LG-AI-EXAONE/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 reorder the LayerNorm position from the traditional Pre-LN scheme by applying LayerNorm directly to the attention and MLP outputs, 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](https://arxiv.org/abs/2507.11407), [HuggingFace paper](https://huggingface.co/papers/2507.11407), [blog](https://www.lgresearch.ai/blog/view?seq=576), and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-4.0). ### Model Configuration - Number of Parameters (without embeddings): 30.95B - Number of Layers: 64 - Number of Attention Heads: GQA with 40-heads and 8-KV heads - Vocab Size: 102,400 - Context Length: 131,072 tokens - Quantization: GPTQ with 4-bit group-wise weight-only quantization (W4A16g128) ## Quickstart You should install the transformers library forked from the original, available in our [PR](https://github.com/huggingface/transformers/pull/39129). Once this PR is merged and released, we will update this section. You can install the latest version of transformers with support for EXAONE 4.0 by following the command: ```bash pip install git+https://github.com/lgai-exaone/transformers@add-exaone4 ``` ### Non-reasoning mode For general use, you can use the EXAONE 4.0 models with the following example: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "LGAI-EXAONE/EXAONE-4.0-32B-GPTQ" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="bfloat16", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) # choose your prompt prompt = "Explain how wonderful you are" prompt = "Explica lo increíble que eres" prompt = "너가 얼마나 대단한지 설명해 봐" messages = [ {"role": "user", "content": prompt} ] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt" ) output = model.generate( input_ids.to(model.device), max_new_tokens=128, do_sample=False, ) print(tokenizer.decode(output[0])) ``` ### Reasoning mode The EXAONE 4.0 models have reasoning capabilities for handling complex problems. You can activate reasoning mode by using the `enable_thinking=True` argument with the tokenizer, which opens a reasoning block that starts with `` tag without closing it. ```python messages = [ {"role": "user", "content": "Which one is bigger, 3.12 vs 3.9?"} ] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", enable_thinking=True, ) output = model.generate( input_ids.to(model.device), max_new_tokens=128, do_sample=True, temperature=0.6, top_p=0.95 ) print(tokenizer.decode(output[0])) ``` > [!IMPORTANT] > The model generation with reasoning mode can be affected sensitively by sampling parameters, so please refer to the [Usage Guideline](#usage-guideline) for better quality. ### Agentic tool use The EXAONE 4.0 models can be used as agents with their tool calling capabilities. You can provide tool schemas to the model for effective tool calling. ```python import random def roll_dice(max_num: int): return random.randint(1, max_num) tools = [ { "type": "function", "function": { "name": "roll_dice", "description": "Roll a dice with the number 1 to N. User can select the number N.", "parameters": { "type": "object", "required": ["max_num"], "properties": { "max_num": { "type": "int", "description": "Max number of the dice" } } } } } ] messages = [ {"role": "user", "content": "Roll D6 dice twice!"} ] input_ids = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", tools=tools, ) output = model.generate( input_ids.to(model.device), max_new_tokens=1024, do_sample=True, temperature=0.6, top_p=0.95, ) print(tokenizer.decode(output[0])) ``` ## 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](https://arxiv.org/abs/2507.11407). - ✅ 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](https://huggingface.co/datasets/LGAI-EXAONE/KMMLU-Redux) and [KMMLU-Pro](https://huggingface.co/datasets/LGAI-EXAONE/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 > [!IMPORTANT] > 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 `` 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](./LICENSE) > [!NOTE] > 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: contact_us@lgresearch.ai

	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

	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