--- base_model: LGAI-EXAONE/EXAONE-4.0-32B base_model_relation: finetune 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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πŸ“’ EXAONE 4.0 is officially supported by HuggingFace transformers! Please check out the guide below
# EXAONE-4.0.1-32B *The version 4.0.1 is a patch version to reduce unintended or inappropriate responses.* ## 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 ## Quickstart You should install the transformers library with version >= `4.54.0`. ### 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-4.0.1-32B" 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 increΓ­ble que eres" prompt = "λ„ˆκ°€ μ–Όλ§ˆλ‚˜ λŒ€λ‹¨ν•œμ§€ μ„€λͺ…ν•΄ 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])) ``` ## Deployment ### TensorRT-LLM TensorRT-LLM officially supports EXAONE 4.0 models in the latest commits. Before it is released, you need to clone the TensorRT-LLM repository to build from source. ```bash git clone https://github.com/NVIDIA/TensorRT-LLM.git ``` After cloning the repository, you need to build the source for installation. Please refer to [the official documentation](https://nvidia.github.io/TensorRT-LLM/installation/build-from-source-linux.html) for a guide to build the TensorRT-LLM environment. You can run the TensorRT-LLM server by following steps: 1. Write extra configuration YAML file ```yaml # extra_llm_api_config.yaml kv_cache_config: enable_block_reuse: false ``` 2. Run server with the configuration ```bash trtllm-serve serve [MODEL_PATH] --backend pytorch --extra_llm_api_options extra_llm_api_config.yaml ``` For more details, please refer to [the documentation](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/exaone) of EXAONE from TensorRT-LLM. > [!NOTE] > Other inference engines including `vllm` and `sglang` don't support the EXAONE 4.0 officially now. We will update as soon as these libraries are updated. ## 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). - βœ… 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! ### 32B Reasoning Mode
EXAONE 4.0.1 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 βœ… βœ… βœ…
World Knowledge
MMLU-Pro 81.8 76.0 73.4 80.0 83.0 85.0
GPQA-Diamond 74.3 68.9 68.2 68.4 71.1 81.0
Math/Coding
AIME 2025 84.5 78.0 62.8 72.9 81.5 87.5
LiveCodeBench v6 67.7 47.1 47.4 60.1 58.9 70.3
Instruction Following
IFEval 82.3 84.9 37.9 85.0 83.4 80.8
Agentic Tool Use
BFCL-v3 60.7 N/A 40.4 70.3 70.8 64.7
Tau-Bench (Airline) 48.0 N/A 38.5 34.5 37.5 53.5
Tau-Bench (Retail) 65.4 N/A 10.2 55.2 58.3 63.9
Multilinguality
KMMLU-Pro 65.7 55.8 51.5 61.4 68.1 71.7
KSM 87.0 79.8 71.9 82.8 86.2 86.7
MMMLU (ES) 85.4 84.3 68.9 82.8 86.7 88.2
### 32B Non-Reasoning Mode
EXAONE 4.0.1 32B Phi 4 Mistral-Small-2506 Gemma3 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 βœ… βœ… βœ…
World Knowledge
MMLU-Pro 77.4 70.4 69.1 67.5 74.4 77.4 80.5 81.2
GPQA-Diamond 61.6 56.1 46.1 42.4 54.6 62.9 69.8 68.4
Math/Coding
AIME 2025 36.3 17.8 30.2 23.8 20.2 24.7 18.0 50.0
LiveCodeBench v6 43.3 27.4 26.9 29.7 28.0 31.4 32.7 44.0
Instruction Following
IFEval 84.7 63.0 77.8 82.6 83.2 83.2 85.4 81.2
Agentic Tool Use
BFCL-v3 63.9 N/A 57.7 N/A 63.0 68.0 52.9 63.8
Tau-Bench (Airline) 18.5 N/A 36.1 N/A 16.0 27.0 38.0 40.5
Tau-Bench (Retail) 52.0 N/A 35.5 N/A 47.6 56.5 6.5 68.5
Multilinguality
KMMLU-Pro 59.8 44.8 51.0 50.7 58.3 64.4 68.8 67.3
KSM 56.3 29.1 35.5 36.1 41.3 46.6 40.6 63.5
MMMLU (ES) 80.3 81.2 78.4 78.7 82.1 83.7 86.9 86.7
## 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