EXAONE 4.0 32B

🚀 EXAONE-4.0-32B

EXAONE 4.0 integrates a Non-reasoning mode and Reasoning mode, achieving both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. It incorporates essential features for the agentic AI era and extends multilingual capabilities to support Spanish in addition to English and Korean. The model series includes a 32B high - performance model and a 1.2B on - device model.

🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗
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✨ Features

• Integrated Modes: Combines Non - reasoning mode and Reasoning mode to balance usability and advanced reasoning.
• Multilingual Support: Supports English, Korean, and Spanish.
• Agentic Tool Use: Capable of using tools effectively for various tasks.
• New Architectural Changes: Adopts Hybrid Attention and QK - Reorder - Norm for better performance.

📦 Installation

You should install the transformers library forked from the original, available in our PR. 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:

pip install git+https://github.com/lgai-exaone/transformers@add-exaone4

💻 Usage Examples

Basic Usage

Non - reasoning mode

For general use, you can use the EXAONE 4.0 models with the following example:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "LGAI-EXAONE/EXAONE-4.0-32B"

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 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]))

Advanced Usage

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 <think> tag without closing it.

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 Note

The model generation with reasoning mode can be affected sensitively by sampling parameters, so please refer to the 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.

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]))

📚 Documentation

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.

Model Configuration

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.

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 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

   # extra_llm_api_config.yaml
   kv_cache_config:
     enable_block_reuse: false
   

2. Run server with the configuration

   trtllm-serve serve [MODEL_PATH] --backend pytorch --extra_llm_api_options extra_llm_api_config.yaml
   

💡 Usage Tip

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.

• ✅ 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!

32B Reasoning Mode

32B Non - Reasoning Mode

🔧 Technical Details

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.

📄 License

License: other License Name: exaone License Link: LICENSE