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LICENSE

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+EXAONE AI Model License Agreement 1.2 - NC
+
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README.md

@@ -0,0 +1,1095 @@
+---
+base_model: LGAI-EXAONE/EXAONE-4.0-1.2B
+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
+---
+
+<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>
+
+# EXAONE-4.0-1.2B-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](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 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](https://www.lgresearch.ai/data/cdn/upload/EXAONE_4_0.pdf), [blog](#), and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-4.0).
+
+
+### 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](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md).
+
+```bash
+git clone --single-branch -b add-exaone4 https://github.com/lgai-exaone/llama.cpp.git
+```
+
+2. Download the EXAONE 4.0 model weights in GGUF format.
+
+    ```bash
+    huggingface-cli download LGAI-EXAONE/EXAONE-4.0-1.2B-GGUF-GGUF \
+        --include "EXAONE-4.0-1.2B-GGUF-Q4_K_M.gguf" \
+        --local-dir .
+    ```
+
+
+<details>
+<summary>Generation with `llama-cli`</summary>
+
+3. 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](https://github.com/ggml-org/llama.cpp/pull/14630). We will update this once these issues are solved.
+
+    ```python
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    model_name = "LGAI-EXAONE/EXAONE-4.0-1.2B-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)
+    ```
+
+4. Generate result with greedy decoding.
+    ```bash
+    llama-cli -m EXAONE-4.0-1.2B-GGUF-Q4_K_M.gguf \
+        -fa -ngl 64 \
+        --temp 0.0 --top-k 1 \
+        -f inputs.txt -no-cnv
+    ```
+
+</details>
+
+<details>
+<summary>OpenAI compatible server with `llama-server`</summary>
+
+3. Run llama-server with EXAONE 4.0 Jinja template.
+    ```bash
+    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
+    ```
+
+4. 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. 
+    
+    ```bash
+    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
+        }'
+    ```
+
+</details>
+
+
+## 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://www.lgresearch.ai/data/cdn/upload/EXAONE_4_0.pdf).
+
+- ✅ denotes the model has a hybrid reasoning capability, evaluated by selecting reasoning / non-reasoning on the purpose.
+- The evaluation results are based on the original model, not quantized model.
+
+
+### 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>Gemma 3 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>SmolLM3 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>SmolLM3 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
+
+TBD
+
+
+## Contact
+
+LG AI Research Technical Support: [email protected]