Update README.md

README.md

@@ -1,199 +1,1182 @@
 ---
+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
-tags: []
 ---
 
-# Model Card for Model ID
+<p align="center">
+<img src="assets/EXAONE_Symbol+BI_3d.png", width="300", style="margin: 40 auto;">
+🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗
+<br>✈️ Try on <a href="https://friendli.ai/suite/~/serverless-endpoints/LGAI-EXAONE/EXAONE-4.0-32B/overview">FriendliAI</a> (licensed under commercial purposes)
+<br><br><i>📢 EXAONE 4.0 is officially supported by HuggingFace transformers! Please check out the guide <a href="#quickstart">below</a></i>
+<br>
 
-<!-- Provide a quick summary of what the model is/does. -->
+# EXAONE-4.0-32B
 
+## Introduction
 
+EXAONE 4.0 bnb 4bit 모델 
 
-## Model Details
+https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
 
-### Model Description
+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. 
 
-<!-- Provide a longer summary of what this model is. -->
+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.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+In the EXAONE 4.0 architecture, we apply new architectural changes compared to previous EXAONE models as below:
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+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.
 
-### Model Sources [optional]
+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).
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Model Configuration
 
-## Uses
+- 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
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
+## Quickstart
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+You should install the transformers library with version >= `4.54.0`.
 
-[More Information Needed]
+### 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"
+
+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
 
-### Downstream Use [optional]
+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.
+
+```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,
+)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+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]))
+```
 
-[More Information Needed]
+> [!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.
 
-### Out-of-Scope Use
+### Agentic tool use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+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.
 
-[More Information Needed]
+```python
+import random
 
-## Bias, Risks, and Limitations
+def roll_dice(max_num: int):
+    return random.randint(1, max_num)
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+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 LGAI-EXAONE/EXAONE-4.0-32B --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.
+
+### vLLM
+
+vLLM officially supports EXAONE 4.0 models in the version of `0.10.0`. You can run the vLLM server by following command:
+
+```bash
+vllm serve LGAI-EXAONE/EXAONE-4.0-32B --enable-auto-tool-choice --tool-call-parser hermes --reasoning-parser deepseek_r1
+```
+
+For more details, please refer to [the vLLM documentation](https://docs.vllm.ai/en/stable/).
+
+> [!NOTE]
+> Other inference engines including `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).
+
+- ✅ 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
+
+<table>
+    <tr>
+        <th> </th>
+        <th>EXAONE 4.0 32B </th>
+        <th>Phi 4 reasoning-plus</th>
+        <th>Magistral Small-2506</th>
+        <th>Qwen 3 32B </th>
+        <th>Qwen 3 235B </th>
+        <th>DeepSeek R1-0528</th>
+    </tr>
+    <tr>
+        <td align="center">Model Size</td>
+        <td align="center">32.0B</td>
+        <td align="center">14.7B</td>
+        <td align="center">23.6B</td>
+        <td align="center">32.8B</td>
+        <td align="center">235B</td>
+        <td align="center">671B</td>
+    </tr>
+    <tr>
+        <td align="center">Hybrid Reasoning</td>
+        <td align="center">✅</td>
+        <td align="center"> </td>
+        <td align="center"> </td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center"> </td>
+    </tr>
+    <tr>
+        <td align="center" colspan='7'><i>World Knowledge</i></td>
+    </tr>
+    <tr>
+        <td >MMLU-Redux</td>
+        <td align="center">92.3</td>
+        <td align="center">90.8</td>
+        <td align="center">86.8</td>
+        <td align="center">90.9</td>
+        <td align="center">92.7</td>
+        <td align="center">93.4</td>
+    </tr>
+    <tr>
+        <td >MMLU-Pro</td>
+        <td align="center">81.8</td>
+        <td align="center">76.0</td>
+        <td align="center">73.4</td>
+        <td align="center">80.0</td>
+        <td align="center">83.0</td>
+        <td align="center">85.0</td>
+    </tr>
+    <tr>
+        <td >GPQA-Diamond</td>
+        <td align="center">75.4</td>
+        <td align="center">68.9</td>
+        <td align="center">68.2</td>
+        <td align="center">68.4</td>
+        <td align="center">71.1</td>
+        <td align="center">81.0</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='7'><i>Math/Coding</i></td>
+    </tr>
+    <tr>
+        <td >AIME 2025</td>
+        <td align="center">85.3</td>
+        <td align="center">78.0</td>
+        <td align="center">62.8</td>
+        <td align="center">72.9</td>
+        <td align="center">81.5</td>
+        <td align="center">87.5</td>
+    </tr>
+    <tr>
+        <td >HMMT Feb 2025</td>
+        <td align="center">72.9</td>
+        <td align="center">53.6</td>
+        <td align="center">43.5</td>
+        <td align="center">50.4</td>
+        <td align="center">62.5</td>
+        <td align="center">79.4</td>
+    </tr>
+    <tr>
+        <td >LiveCodeBench v5</td>
+        <td align="center">72.6</td>
+        <td align="center">51.7</td>
+        <td align="center">55.8</td>
+        <td align="center">65.7</td>
+        <td align="center">70.7</td>
+        <td align="center">75.2</td>
+    </tr>
+    <tr>
+        <td >LiveCodeBench v6</td>
+        <td align="center">66.7</td>
+        <td align="center">47.1</td>
+        <td align="center">47.4</td>
+        <td align="center">60.1</td>
+        <td align="center">58.9</td>
+        <td align="center">70.3</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='7'><i>Instruction Following</i></td>
+    </tr>
+    <tr>
+        <td >IFEval</td>
+        <td align="center">83.7</td>
+        <td align="center">84.9</td>
+        <td align="center">37.9</td>
+        <td align="center">85.0</td>
+        <td align="center">83.4</td>
+        <td align="center">80.8</td>
+    </tr>
+    <tr>
+        <td >Multi-IF (EN)</td>
+        <td align="center">73.5</td>
+        <td align="center">56.1</td>
+        <td align="center">27.4</td>
+        <td align="center">73.4</td>
+        <td align="center">73.4</td>
+        <td align="center">72.0</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='7'><i>Agentic Tool Use</i></td>
+    </tr>
+    <tr>
+        <td >BFCL-v3</td>
+        <td align="center">63.9</td>
+        <td align="center">N/A</td>
+        <td align="center">40.4</td>
+        <td align="center">70.3</td>
+        <td align="center">70.8</td>
+        <td align="center">64.7</td>
+    </tr>
+    <tr>
+        <td >Tau-Bench (Airline)</td>
+        <td align="center">51.5</td>
+        <td align="center">N/A</td>
+        <td align="center">38.5</td>
+        <td align="center">34.5</td>
+        <td align="center">37.5</td>
+        <td align="center">53.5</td>
+    </tr>
+    <tr>
+        <td >Tau-Bench (Retail)</td>
+        <td align="center">62.8</td>
+        <td align="center">N/A</td>
+        <td align="center">10.2</td>
+        <td align="center">55.2</td>
+        <td align="center">58.3</td>
+        <td align="center">63.9</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='7'><i>Multilinguality</i></td>
+    </tr>
+    <tr>
+        <td >KMMLU-Pro</td>
+        <td align="center">67.7</td>
+        <td align="center">55.8</td>
+        <td align="center">51.5</td>
+        <td align="center">61.4</td>
+        <td align="center">68.1</td>
+        <td align="center">71.7</td>
+    </tr>
+    <tr>
+        <td >KMMLU-Redux</td>
+        <td align="center">72.7</td>
+        <td align="center">62.7</td>
+        <td align="center">54.6</td>
+        <td align="center">67.5</td>
+        <td align="center">74.5</td>
+        <td align="center">77.0</td>
+    </tr>
+    <tr>
+        <td >KSM</td>
+        <td align="center">87.6</td>
+        <td align="center">79.8</td>
+        <td align="center">71.9</td>
+        <td align="center">82.8</td>
+        <td align="center">86.2</td>
+        <td align="center">86.7</td>
+    </tr>
+    <tr>
+        <td >MMMLU (ES)</td>
+        <td align="center">85.6</td>
+        <td align="center">84.3</td>
+        <td align="center">68.9</td>
+        <td align="center">82.8</td>
+        <td align="center">86.7</td>
+        <td align="center">88.2</td>
+    </tr>
+    <tr>
+        <td >MATH500 (ES)</td>
+        <td align="center">95.8</td>
+        <td align="center">94.2</td>
+        <td align="center">83.5</td>
+        <td align="center">94.3</td>
+        <td align="center">95.1</td>
+        <td align="center">96.0</td>
+    </tr>
+</table>
+
+### 32B Non-Reasoning Mode
+
+<table>
+    <tr>
+        <th> </th>
+        <th>EXAONE 4.0 32B </th>
+        <th>Phi 4</th>
+        <th>Mistral-Small-2506</th>
+        <th>Gemma3 27B</th>
+        <th>Qwen3 32B </th>
+        <th>Qwen3 235B </th>
+        <th>Llama-4-Maverick</th>
+        <th>DeepSeek V3-0324</th>
+    </tr>
+    <tr>
+        <td align="center">Model Size</td>
+        <td align="center">32.0B</td>
+        <td align="center">14.7B</td>
+        <td align="center">24.0B</td>
+        <td align="center">27.4B</td>
+        <td align="center">32.8B</td>
+        <td align="center">235B</td>
+        <td align="center">402B</td>
+        <td align="center">671B</td>
+    </tr>
+    <tr>
+        <td align="center">Hybrid Reasoning</td>
+        <td align="center">✅</td>
+        <td align="center"> </td>
+        <td align="center"> </td>
+        <td align="center"> </td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center"> </td>
+        <td align="center"> </td>
+    </tr>
+    <tr>
+        <td align="center" colspan='9'><i>World Knowledge</i></td>
+    </tr>
+    <tr>
+        <td >MMLU-Redux</td>
+        <td align="center">89.8</td>
+        <td align="center">88.3</td>
+        <td align="center">85.9</td>
+        <td align="center">85.0</td>
+        <td align="center">85.7</td>
+        <td align="center">89.2</td>
+        <td align="center">92.3</td>
+        <td align="center">92.3</td>
+    </tr>
+    <tr>
+        <td >MMLU-Pro</td>
+        <td align="center">77.6</td>
+        <td align="center">70.4</td>
+        <td align="center">69.1</td>
+        <td align="center">67.5</td>
+        <td align="center">74.4</td>
+        <td align="center">77.4</td>
+        <td align="center">80.5</td>
+        <td align="center">81.2</td>
+    </tr>
+    <tr>
+        <td >GPQA-Diamond</td>
+        <td align="center">63.7</td>
+        <td align="center">56.1</td>
+        <td align="center">46.1</td>
+        <td align="center">42.4</td>
+        <td align="center">54.6</td>
+        <td align="center">62.9</td>
+        <td align="center">69.8</td>
+        <td align="center">68.4</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='9'><i>Math/Coding</i></td>
+    </tr>
+    <tr>
+        <td >AIME 2025</td>
+        <td align="center">35.9</td>
+        <td align="center">17.8</td>
+        <td align="center">30.2</td>
+        <td align="center">23.8</td>
+        <td align="center">20.2</td>
+        <td align="center">24.7</td>
+        <td align="center">18.0</td>
+        <td align="center">50.0</td>
+    </tr>
+    <tr>
+        <td >HMMT Feb 2025</td>
+        <td align="center">21.8</td>
+        <td align="center">4.0</td>
+        <td align="center">16.9</td>
+        <td align="center">10.3</td>
+        <td align="center">9.8</td>
+        <td align="center">11.9</td>
+        <td align="center">7.3</td>
+        <td align="center">29.2</td>
+    </tr>
+    <tr>
+        <td >LiveCodeBench v5</td>
+        <td align="center">43.3</td>
+        <td align="center">24.6</td>
+        <td align="center">25.8</td>
+        <td align="center">27.5</td>
+        <td align="center">31.3</td>
+        <td align="center">35.3</td>
+        <td align="center">43.4</td>
+        <td align="center">46.7</td>
+    </tr>
+    <tr>
+        <td >LiveCodeBench v6</td>
+        <td align="center">43.1</td>
+        <td align="center">27.4</td>
+        <td align="center">26.9</td>
+        <td align="center">29.7</td>
+        <td align="center">28.0</td>
+        <td align="center">31.4</td>
+        <td align="center">32.7</td>
+        <td align="center">44.0</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='9'><i>Instruction Following</i></td>
+    </tr>
+    <tr>
+        <td >IFEval</td>
+        <td align="center">84.8</td>
+        <td align="center">63.0</td>
+        <td align="center">77.8</td>
+        <td align="center">82.6</td>
+        <td align="center">83.2</td>
+        <td align="center">83.2</td>
+        <td align="center">85.4</td>
+        <td align="center">81.2</td>
+    </tr>
+    <tr>
+        <td >Multi-IF (EN)</td>
+        <td align="center">71.6</td>
+        <td align="center">47.7</td>
+        <td align="center">63.2</td>
+        <td align="center">72.1</td>
+        <td align="center">71.9</td>
+        <td align="center">72.5</td>
+        <td align="center">77.9</td>
+        <td align="center">68.3</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='9'><i>Long Context</i></td>
+    </tr>
+    <tr>
+        <td >HELMET</td>
+        <td align="center">58.3</td>
+        <td align="center">N/A</td>
+        <td align="center">61.9</td>
+        <td align="center">58.3</td>
+        <td align="center">54.5</td>
+        <td align="center">63.3</td>
+        <td align="center">13.7</td>
+        <td align="center">N/A</td>
+    </tr>
+    <tr>
+        <td >RULER</td>
+        <td align="center">88.2</td>
+        <td align="center">N/A</td>
+        <td align="center">71.8</td>
+        <td align="center">66.0</td>
+        <td align="center">85.6</td>
+        <td align="center">90.6</td>
+        <td align="center">2.9</td>
+        <td align="center">N/A</td>
+    </tr>
+    <tr>
+        <td >LongBench v1</td>
+        <td align="center">48.1</td>
+        <td align="center">N/A</td>
+        <td align="center">51.5</td>
+        <td align="center">51.5</td>
+        <td align="center">44.2</td>
+        <td align="center">45.3</td>
+        <td align="center">34.7</td>
+        <td align="center">N/A</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='9'><i>Agentic Tool Use</i></td>
+    </tr>
+    <tr>
+        <td >BFCL-v3</td>
+        <td align="center">65.2</td>
+        <td align="center">N/A</td>
+        <td align="center">57.7</td>
+        <td align="center">N/A</td>
+        <td align="center">63.0</td>
+        <td align="center">68.0</td>
+        <td align="center">52.9</td>
+        <td align="center">63.8</td>
+    </tr>
+    <tr>
+        <td >Tau-Bench (Airline)</td>
+        <td align="center">25.5</td>
+        <td align="center">N/A</td>
+        <td align="center">36.1</td>
+        <td align="center">N/A</td>
+        <td align="center">16.0</td>
+        <td align="center">27.0</td>
+        <td align="center">38.0</td>
+        <td align="center">40.5</td>
+    </tr>
+    <tr>
+        <td >Tau-Bench (Retail)</td>
+        <td align="center">55.9</td>
+        <td align="center">N/A</td>
+        <td align="center">35.5</td>
+        <td align="center">N/A</td>
+        <td align="center">47.6</td>
+        <td align="center">56.5</td>
+        <td align="center">6.5</td>
+        <td align="center">68.5</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='9'><i>Multilinguality</i></td>
+    </tr>
+    <tr>
+        <td >KMMLU-Pro</td>
+        <td align="center">60.0</td>
+        <td align="center">44.8</td>
+        <td align="center">51.0</td>
+        <td align="center">50.7</td>
+        <td align="center">58.3</td>
+        <td align="center">64.4</td>
+        <td align="center">68.8</td>
+        <td align="center">67.3</td>
+    </tr>
+    <tr>
+        <td >KMMLU-Redux</td>
+        <td align="center">64.8</td>
+        <td align="center">50.1</td>
+        <td align="center">53.6</td>
+        <td align="center">53.3</td>
+        <td align="center">64.4</td>
+        <td align="center">71.7</td>
+        <td align="center">76.9</td>
+        <td align="center">72.2</td>
+    </tr>
+    <tr>
+        <td >KSM</td>
+        <td align="center">59.8</td>
+        <td align="center">29.1</td>
+        <td align="center">35.5</td>
+        <td align="center">36.1</td>
+        <td align="center">41.3</td>
+        <td align="center">46.6</td>
+        <td align="center">40.6</td>
+        <td align="center">63.5</td>
+    </tr>
+    <tr>
+        <td >Ko-LongBench</td>
+        <td align="center">76.9</td>
+        <td align="center">N/A</td>
+        <td align="center">55.4</td>
+        <td align="center">72.0</td>
+        <td align="center">73.9</td>
+        <td align="center">74.6</td>
+        <td align="center">65.6</td>
+        <td align="center">N/A</td>
+    </tr>
+    <tr>
+        <td >MMMLU (ES)</td>
+        <td align="center">80.6</td>
+        <td align="center">81.2</td>
+        <td align="center">78.4</td>
+        <td align="center">78.7</td>
+        <td align="center">82.1</td>
+        <td align="center">83.7</td>
+        <td align="center">86.9</td>
+        <td align="center">86.7</td>
+    </tr>
+    <tr>
+        <td >MATH500 (ES)</td>
+        <td align="center">87.3</td>
+        <td align="center">78.2</td>
+        <td align="center">83.4</td>
+        <td align="center">86.8</td>
+        <td align="center">84.7</td>
+        <td align="center">87.2</td>
+        <td align="center">78.7</td>
+        <td align="center">89.2</td>
+    </tr>
+    <tr>
+        <td >WMT24++ (ES)</td>
+        <td align="center">90.7</td>
+        <td align="center">89.3</td>
+        <td align="center">92.2</td>
+        <td align="center">93.1</td>
+        <td align="center">91.4</td>
+        <td align="center">92.9</td>
+        <td align="center">92.7</td>
+        <td align="center">94.3 </td>
+    </tr>
+</table>
+
+### 1.2B Reasoning Mode
+
+<table>
+    <tr>
+        <th> </th>
+        <th>EXAONE 4.0 1.2B </th>
+        <th>EXAONE Deep 2.4B</th>
+        <th>Qwen 3 0.6B </th>
+        <th>Qwen 3 1.7B </th>
+        <th>SmolLM 3 3B </th>
+    </tr>
+    <tr>
+        <td align="center">Model Size</td>
+        <td align="center">1.28B</td>
+        <td align="center">2.41B</td>
+        <td align="center">596M</td>
+        <td align="center">1.72B</td>
+        <td align="center">3.08B</td>
+    </tr>
+    <tr>
+        <td align="center">Hybrid Reasoning</td>
+        <td align="center">✅</td>
+        <td align="center"> </td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>World Knowledge</i></td>
+    </tr>
+    <tr>
+        <td >MMLU-Redux</td>
+        <td align="center">71.5</td>
+        <td align="center">68.9</td>
+        <td align="center">55.6</td>
+        <td align="center">73.9</td>
+        <td align="center">74.8</td>
+    </tr>
+    <tr>
+        <td >MMLU-Pro</td>
+        <td align="center">59.3</td>
+        <td align="center">56.4</td>
+        <td align="center">38.3</td>
+        <td align="center">57.7</td>
+        <td align="center">57.8</td>
+    </tr>
+    <tr>
+        <td >GPQA-Diamond</td>
+        <td align="center">52.0</td>
+        <td align="center">54.3</td>
+        <td align="center">27.9</td>
+        <td align="center">40.1</td>
+        <td align="center">41.7</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Math/Coding</i></td>
+    </tr>
+    <tr>
+        <td >AIME 2025</td>
+        <td align="center">45.2</td>
+        <td align="center">47.9</td>
+        <td align="center">15.1</td>
+        <td align="center">36.8</td>
+        <td align="center">36.7</td>
+    </tr>
+    <tr>
+        <td >HMMT Feb 2025</td>
+        <td align="center">34.0</td>
+        <td align="center">27.3</td>
+        <td align="center">7.0</td>
+        <td align="center">21.8</td>
+        <td align="center">26.0</td>
+    </tr>
+    <tr>
+        <td >LiveCodeBench v5</td>
+        <td align="center">44.6</td>
+        <td align="center">47.2</td>
+        <td align="center">12.3</td>
+        <td align="center">33.2</td>
+        <td align="center">27.6</td>
+    </tr>
+    <tr>
+        <td >LiveCodeBench v6</td>
+        <td align="center">45.3</td>
+        <td align="center">43.1</td>
+        <td align="center">16.4</td>
+        <td align="center">29.9</td>
+        <td align="center">29.1</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Instruction Following</i></td>
+    </tr>
+    <tr>
+        <td >IFEval</td>
+        <td align="center">67.8</td>
+        <td align="center">71.0</td>
+        <td align="center">59.2</td>
+        <td align="center">72.5</td>
+        <td align="center">71.2</td>
+    </tr>
+    <tr>
+        <td >Multi-IF (EN)</td>
+        <td align="center">53.9</td>
+        <td align="center">54.5</td>
+        <td align="center">37.5</td>
+        <td align="center">53.5</td>
+        <td align="center">47.5</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Agentic Tool Use</i></td>
+    </tr>
+    <tr>
+        <td >BFCL-v3</td>
+        <td align="center">52.9</td>
+        <td align="center">N/A</td>
+        <td align="center">46.4</td>
+        <td align="center">56.6</td>
+        <td align="center">37.1</td>
+    </tr>
+    <tr>
+        <td >Tau-Bench (Airline)</td>
+        <td align="center">20.5</td>
+        <td align="center">N/A</td>
+        <td align="center">22.0</td>
+        <td align="center">31.0</td>
+        <td align="center">37.0</td>
+    </tr>
+    <tr>
+        <td >Tau-Bench (Retail)</td>
+        <td align="center">28.1</td>
+        <td align="center">N/A</td>
+        <td align="center">3.3</td>
+        <td align="center">6.5</td>
+        <td align="center">5.4</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Multilinguality</i></td>
+    </tr>
+    <tr>
+        <td >KMMLU-Pro</td>
+        <td align="center">42.7</td>
+        <td align="center">24.6</td>
+        <td align="center">21.6</td>
+        <td align="center">38.3</td>
+        <td align="center">30.5</td>
+    </tr>
+    <tr>
+        <td >KMMLU-Redux</td>
+        <td align="center">46.9</td>
+        <td align="center">25.0</td>
+        <td align="center">24.5</td>
+        <td align="center">38.0</td>
+        <td align="center">33.7</td>
+    </tr>
+    <tr>
+        <td >KSM</td>
+        <td align="center">60.6</td>
+        <td align="center">60.9</td>
+        <td align="center">22.8</td>
+        <td align="center">52.9</td>
+        <td align="center">49.7</td>
+    </tr>
+    <tr>
+        <td >MMMLU (ES)</td>
+        <td align="center">62.4</td>
+        <td align="center">51.4</td>
+        <td align="center">48.8</td>
+        <td align="center">64.5</td>
+        <td align="center">64.7</td>
+    </tr>
+    <tr>
+        <td >MATH500 (ES)</td>
+        <td align="center">88.8</td>
+        <td align="center">84.5</td>
+        <td align="center">70.6</td>
+        <td align="center">87.9</td>
+        <td align="center">87.5 </td>
+    </tr>
+</table>
+
+### 1.2B Non-Reasoning Mode
+
+<table>
+    <tr>
+        <th> </th>
+        <th>EXAONE 4.0 1.2B </th>
+        <th>Qwen 3 0.6B </th>
+        <th>Gemma 3 1B</th>
+        <th>Qwen 3 1.7B </th>
+        <th>SmolLM 3 3B </th>
+    </tr>
+    <tr>
+        <td align="center">Model Size</td>
+        <td align="center">1.28B</td>
+        <td align="center">596M</td>
+        <td align="center">1.00B</td>
+        <td align="center">1.72B</td>
+        <td align="center">3.08B</td>
+    </tr>
+    <tr>
+        <td align="center">Hybrid Reasoning</td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+        <td align="center"> </td>
+        <td align="center">✅</td>
+        <td align="center">✅</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>World Knowledge</i></td>
+    </tr>
+    <tr>
+        <td >MMLU-Redux</td>
+        <td align="center">66.9</td>
+        <td align="center">44.6</td>
+        <td align="center">40.9</td>
+        <td align="center">63.4</td>
+        <td align="center">65.0</td>
+    </tr>
+    <tr>
+        <td >MMLU-Pro</td>
+        <td align="center">52.0</td>
+        <td align="center">26.6</td>
+        <td align="center">14.7</td>
+        <td align="center">43.7</td>
+        <td align="center">43.6</td>
+    </tr>
+    <tr>
+        <td >GPQA-Diamond</td>
+        <td align="center">40.1</td>
+        <td align="center">22.9</td>
+        <td align="center">19.2</td>
+        <td align="center">28.6</td>
+        <td align="center">35.7</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Math/Coding</i></td>
+    </tr>
+    <tr>
+        <td >AIME 2025</td>
+        <td align="center">23.5</td>
+        <td align="center">2.6</td>
+        <td align="center">2.1</td>
+        <td align="center">9.8</td>
+        <td align="center">9.3</td>
+    </tr>
+    <tr>
+        <td >HMMT Feb 2025</td>
+        <td align="center">13.0</td>
+        <td align="center">1.0</td>
+        <td align="center">1.5</td>
+        <td align="center">5.1</td>
+        <td align="center">4.7</td>
+    </tr>
+    <tr>
+        <td >LiveCodeBench v5</td>
+        <td align="center">26.4</td>
+        <td align="center">3.6</td>
+        <td align="center">1.8</td>
+        <td align="center">11.6</td>
+        <td align="center">11.4</td>
+    </tr>
+    <tr>
+        <td >LiveCodeBench v6</td>
+        <td align="center">30.1</td>
+        <td align="center">6.9</td>
+        <td align="center">2.3</td>
+        <td align="center">16.6</td>
+        <td align="center">20.6</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Instruction Following</i></td>
+    </tr>
+    <tr>
+        <td >IFEval</td>
+        <td align="center">74.7</td>
+        <td align="center">54.5</td>
+        <td align="center">80.2</td>
+        <td align="center">68.2</td>
+        <td align="center">76.7</td>
+    </tr>
+    <tr>
+        <td >Multi-IF (EN)</td>
+        <td align="center">62.1</td>
+        <td align="center">37.5</td>
+        <td align="center">32.5</td>
+        <td align="center">51.0</td>
+        <td align="center">51.9</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Long Context</i></td>
+    </tr>
+    <tr>
+        <td >HELMET</td>
+        <td align="center">41.2</td>
+        <td align="center">21.1</td>
+        <td align="center">N/A</td>
+        <td align="center">33.8</td>
+        <td align="center">38.6</td>
+    </tr>
+    <tr>
+        <td >RULER</td>
+        <td align="center">77.4</td>
+        <td align="center">55.1</td>
+        <td align="center">N/A</td>
+        <td align="center">65.9</td>
+        <td align="center">66.3</td>
+    </tr>
+    <tr>
+        <td >LongBench v1</td>
+        <td align="center">36.9</td>
+        <td align="center">32.4</td>
+        <td align="center">N/A</td>
+        <td align="center">41.9</td>
+        <td align="center">39.9</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Agentic Tool Use</i></td>
+    </tr>
+    <tr>
+        <td >BFCL-v3</td>
+        <td align="center">55.7</td>
+        <td align="center">44.1</td>
+        <td align="center">N/A</td>
+        <td align="center">52.2</td>
+        <td align="center">47.3</td>
+    </tr>
+    <tr>
+        <td >Tau-Bench (Airline)</td>
+        <td align="center">10.0</td>
+        <td align="center">31.5</td>
+        <td align="center">N/A</td>
+        <td align="center">13.5</td>
+        <td align="center">38.0</td>
+    </tr>
+    <tr>
+        <td >Tau-Bench (Retail)</td>
+        <td align="center">21.7</td>
+        <td align="center">5.7</td>
+        <td align="center">N/A</td>
+        <td align="center">4.6</td>
+        <td align="center">6.7</td>
+    </tr>
+    <tr>
+        <td align="center" colspan='6'><i>Multilinguality</i></td>
+    </tr>
+    <tr>
+        <td >KMMLU-Pro</td>
+        <td align="center">37.5</td>
+        <td align="center">24.6</td>
+        <td align="center">9.7</td>
+        <td align="center">29.5</td>
+        <td align="center">27.6</td>
+    </tr>
+    <tr>
+        <td >KMMLU-Redux</td>
+        <td align="center">40.4</td>
+        <td align="center">22.8</td>
+        <td align="center">19.4</td>
+        <td align="center">29.8</td>
+        <td align="center">26.4</td>
+    </tr>
+    <tr>
+        <td >KSM</td>
+        <td align="center">26.3</td>
+        <td align="center">0.1</td>
+        <td align="center">22.8</td>
+        <td align="center">16.3</td>
+        <td align="center">16.1</td>
+    </tr>
+    <tr>
+        <td >Ko-LongBench</td>
+        <td align="center">69.8</td>
+        <td align="center">16.4</td>
+        <td align="center">N/A</td>
+        <td align="center">57.1</td>
+        <td align="center">15.7</td>
+    </tr>
+    <tr>
+        <td >MMMLU (ES)</td>
+        <td align="center">54.6</td>
+        <td align="center">39.5</td>
+        <td align="center">35.9</td>
+        <td align="center">54.3</td>
+        <td align="center">55.1</td>
+    </tr>
+    <tr>
+        <td >MATH500 (ES)</td>
+        <td align="center">71.2</td>
+        <td align="center">38.5</td>
+        <td align="center">41.2</td>
+        <td align="center">66.0</td>
+        <td align="center">62.4</td>
+    </tr>
+    <tr>
+        <td >WMT24++ (ES)</td>
+        <td align="center">65.9</td>
+        <td align="center">58.2</td>
+        <td align="center">76.9</td>
+        <td align="center">76.7</td>
+        <td align="center">84.0 </td>
+    </tr>
+</table>
+
+
+
+## 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 `<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](./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