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LICENSE

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+EXAONE AI Model License Agreement 1.2 - NC
+
+This License Agreement (“Agreement”) is entered into between you (“Licensee”) and LG Management Development
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README.md

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+---
+base_model: LGAI-EXAONE/EXAONE-4.5-33B
+base_model_relation: quantized
+license: other
+license_name: exaone
+license_link: LICENSE
+language:
+  - en
+  - ko
+  - es
+  - de
+  - ja
+  - vi
+tags:
+  - lg-ai
+  - exaone
+pipeline_tag: image-text-to-text
+library_name: transformers
+---
+
+<br>
+<br>
+<p align="center">
+<img src="assets/EXAONE_Symbol+BI_3d.png" width="400">
+<br>
+<br>
+<br>
+
+<div align="center">
+  <a href="https://huggingface.co/collections/LGAI-EXAONE/exaone-45" style="text-decoration: none;">
+    <img src="https://img.shields.io/badge/🤗-HuggingFace-FC926C?style=for-the-badge" alt="HuggingFace">
+  </a>
+  <a href="https://www.lgresearch.ai/blog/view?seq=641" style="text-decoration: none;">
+    <img src="https://img.shields.io/badge/📝-Blog-E343BD?style=for-the-badge" alt="Blog">
+  </a>
+  <a href="http://arxiv.org/abs/2604.08644" style="text-decoration: none;">
+    <img src="https://img.shields.io/badge/📑-Technical_Report-684CF4?style=for-the-badge" alt="Technical Report">
+  </a>
+  <a href="https://github.com/LG-AI-EXAONE/EXAONE-4.5" style="text-decoration: none;">
+    <img src="https://img.shields.io/badge/🖥️-GitHub-2B3137?style=for-the-badge" alt="GitHub">
+  </a>
+  <!-- <a href="#" style="text-decoration: none;">
+    <img src="https://img.shields.io/badge/✈️_API-Try_on_FriendliAI-2649BC?style=for-the-badge" alt="FriendliAI">
+  </a> -->
+</div>
+
+
+
+
+<br><br>
+
+# EXAONE 4.5
+
+We introduce EXAONE 4.5, the first open-weight vision language model developed by LG AI Research.
+Integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, we expand the model's capability toward multimodality.
+EXAONE 4.5 features 33 billion parameters in total, including 1.2 billion parameters from the vision encoder. 
+EXAONE 4.5 achieves competitive performance in general benchmark while outperforming SOTA models of similar size in document understanding and Korean contextual reasoning, inheriting powerful language capabilities from our previous language models.
+
+For more details, please refer to the [technical report](http://arxiv.org/abs/2604.08644), [blog](https://www.lgresearch.ai/blog/view?seq=641) and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-4.5).
+
+
+### Model Configuration
+
+- Model Type: Causal Language Model + Vision Encoder
+- Number of Parameters (Language Model): 31.7B
+- Number of Parameters (Vision Encoder): 1.29B
+- Hidden Dimension: 5,120
+- Intermediate size: 27,392
+- Number of Layers: 64 Main layers + 1 MTP layers
+  - Hybrid Attention Pattern: 16 x (3 Sliding window attention + 1 Global attention)
+  - Reordered Norm: Apply normalization after Attention/MLP, and before residual connection
+- Sliding Window Attention
+  - Number of Attention Heads: 40 Q-heads and 8 KV-heads
+  - Head Dimension: 128 for both Q/KV
+  - Sliding Window Size: 4,096
+- Global Attention
+  - Number of Attention Heads: 40 Q-heads and 8 KV-heads
+  - Head Dimension: 128 for both Q/KV
+  - No Rotary Positional Embedding Used (NoPE)
+- Vision Encoder
+  - Grouped Query Attention (GQA)
+  - 2D RoPE for vision embeddings
+- Vocab Size: 153,600
+- Context Length: 262,144 tokens
+- Knowledge Cutoff: Dec 2024 (2024/12)
+- Quantization: AWQ with 4-bit group-wise weight-only quantization (W4A16g128)
+
+
+## Evaluation Results
+
+The following table shows the benchmark results for the original EXAONE 4.5. Detailed evaluation results of the original model can be found in our [technical report](http://arxiv.org/abs/2604.08644).
+
+
+### Vision-Language Tasks
+
+<table>
+	<tr>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;"> </th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">EXAONE 4.5 33B (Reasoning)</th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">GPT-5 mini (Reasoning: high)</th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">Qwen3-VL 32B Thinking</th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">Qwen3-VL 235B Thinking</th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">Qwen3.5 27B (Reasoning)</th>
+	</tr>
+	<tr>
+		<td align="center">Architecture</td>
+		<td align="center">Dense</td>
+		<td align="center">-</td>
+		<td align="center">Dense</td>
+		<td align="center">MoE</td>
+		<td align="center">Dense</td>
+	</tr>
+	<tr>
+		<td align="center">Total Params</td>
+		<td align="center">33B</td>
+		<td align="center">-</td>
+		<td align="center">33B</td>
+		<td align="center">236B</td>
+		<td align="center">27B</td>
+	</tr>
+	<tr>
+		<td align="center">Active Params</td>
+		<td align="center">33B</td>
+		<td align="center">-</td>
+		<td align="center">33B</td>
+		<td align="center">22B</td>
+		<td align="center">27B</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>STEM / Puzzle</i></td>
+	</tr>
+	<tr>
+		<td align="center">MMMU</td>
+		<td align="center">78.7</td>
+		<td align="center">79.0</td>
+		<td align="center">78.1</td>
+		<td align="center">80.6</td>
+		<td align="center">82.3</td>
+	</tr>
+	<tr>
+		<td align="center">MMMU-Pro</td>
+		<td align="center">68.6</td>
+		<td align="center">67.3</td>
+		<td align="center">68.1</td>
+		<td align="center">69.3</td>
+		<td align="center">75.0</td>
+	</tr>
+	<tr>
+		<td align="center">MedXpertQA-MM</td>
+		<td align="center">42.1</td>
+		<td align="center">34.4</td>
+		<td align="center">41.6</td>
+		<td align="center">47.6</td>
+		<td align="center">62.4</td>
+	</tr>
+	<tr>
+		<td align="center">MathVision</td>
+		<td align="center">75.2</td>
+		<td align="center">71.9</td>
+		<td align="center">70.2</td>
+		<td align="center">74.6</td>
+		<td align="center">86.0</td>
+	</tr>
+	<tr>
+		<td align="center">MathVista (mini)</td>
+		<td align="center">85.0</td>
+		<td align="center">79.1</td>
+		<td align="center">85.9</td>
+		<td align="center">85.8</td>
+		<td align="center">87.8</td>
+	</tr>
+	<tr>
+		<td align="center">WeMath</td>
+		<td align="center">79.1</td>
+		<td align="center">70.3</td>
+		<td align="center">71.6</td>
+		<td align="center">74.8</td>
+		<td align="center">84.0</td>
+	</tr>
+	<tr>
+		<td align="center">LogicVista</td>
+		<td align="center">73.8</td>
+		<td align="center">70.3</td>
+		<td align="center">70.9</td>
+		<td align="center">72.2</td>
+		<td align="center">77.0</td>
+	</tr>
+	<tr>
+		<td align="center">BabyVision</td>
+		<td align="center">18.8</td>
+		<td align="center">20.9</td>
+		<td align="center">17.4</td>
+		<td align="center">22.2</td>
+		<td align="center">44.6</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Document Understanding</i></td>
+	</tr>
+	<tr>
+		<td align="center">AI2D</td>
+		<td align="center">89.0</td>
+		<td align="center">88.2</td>
+		<td align="center">88.9</td>
+		<td align="center">89.2</td>
+		<td align="center">92.9</td>
+	</tr>
+	<tr>
+		<td align="center">ChartQAPro</td>
+		<td align="center">62.2</td>
+		<td align="center">60.9</td>
+		<td align="center">61.4</td>
+		<td align="center">61.2</td>
+		<td align="center">66.8</td>
+	</tr>
+	<tr>
+		<td align="center">CharXiv (RQ)</td>
+		<td align="center">71.7</td>
+		<td align="center">68.6</td>
+		<td align="center">65.2</td>
+		<td align="center">66.1</td>
+		<td align="center">79.5</td>
+	</tr>
+	<tr>
+		<td align="center">OCRBench v2</td>
+		<td align="center">63.2</td>
+		<td align="center">55.8</td>
+		<td align="center">68.4</td>
+		<td align="center">66.8</td>
+		<td align="center">67.3</td>
+	</tr>
+	<tr>
+		<td align="center">OmniDocBench v1.5</td>
+		<td align="center">81.2</td>
+		<td align="center">77.0</td>
+		<td align="center">83.1</td>
+		<td align="center">84.5</td>
+		<td align="center">88.9</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>General</i></td>
+	</tr>
+	<tr>
+		<td align="center">MMStar</td>
+		<td align="center">74.9</td>
+		<td align="center">74.1</td>
+		<td align="center">79.4</td>
+		<td align="center">78.7</td>
+		<td align="center">81.0</td>
+	</tr>
+	<tr>
+		<td align="center">BLINK</td>
+		<td align="center">68.8</td>
+		<td align="center">67.7</td>
+		<td align="center">68.5</td>
+		<td align="center">67.1</td>
+		<td align="center">71.6</td>
+	</tr>
+	<tr>
+		<td align="center">HallusionBench</td>
+		<td align="center">63.7</td>
+		<td align="center">63.2</td>
+		<td align="center">67.4</td>
+		<td align="center">66.7</td>
+		<td align="center">70.0</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Korean</i></td>
+	</tr>
+	<tr>
+		<td align="center">KMMMU</td>
+		<td align="center">42.7</td>
+		<td align="center">42.6</td>
+		<td align="center">37.8</td>
+		<td align="center">42.1</td>
+		<td align="center">51.7</td>
+	</tr>
+	<tr>
+		<td align="center">K-Viscuit</td>
+		<td align="center">80.1</td>
+		<td align="center">78.5</td>
+		<td align="center">78.5</td>
+		<td align="center">83.9</td>
+		<td align="center">84.0</td>
+	</tr>
+	<tr>
+		<td align="center">KRETA</td>
+		<td align="center">91.9</td>
+		<td align="center">94.8</td>
+		<td align="center">90.3</td>
+		<td align="center">92.8</td>
+		<td align="center">96.5</td>
+	</tr>
+</table>
+
+
+### Language-only Tasks
+
+<table>
+	<tr>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;"> </th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">EXAONE 4.5 33B (Reasoning)</th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">GPT-5 mini (Reasoning: high)</th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">K-EXAONE 236B (Reasoning)</th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">Qwen3-VL 235B Thinking</th>
+		<th style="background: rgba(128,128,128,0.1); text-align: center;">Qwen3.5 27B (Reasoning)</th>
+	</tr>
+	<tr>
+		<td align="center">Architecture</td>
+		<td align="center">Dense</td>
+		<td align="center">-</td>
+		<td align="center">MoE</td>
+		<td align="center">MoE</td>
+		<td align="center">Dense</td>
+	</tr>
+	<tr>
+		<td align="center">Total Params</td>
+		<td align="center">33B</td>
+		<td align="center">-</td>
+		<td align="center">236B</td>
+		<td align="center">236B</td>
+		<td align="center">27B</td>
+	</tr>
+	<tr>
+		<td align="center">Active Params</td>
+		<td align="center">33B</td>
+		<td align="center">-</td>
+		<td align="center">23B</td>
+		<td align="center">22B</td>
+		<td align="center">27B</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Reasoning</i></td>
+	</tr>
+	<tr>
+		<td align="center">AIME 2025</td>
+		<td align="center">92.9</td>
+		<td align="center">91.1</td>
+		<td align="center">92.8</td>
+		<td align="center">89.7</td>
+		<td align="center">93.5</td>
+	</tr>
+	<tr>
+		<td align="center">AIME 2026</td>
+		<td align="center">92.6</td>
+		<td align="center">92.4</td>
+		<td align="center">92.2</td>
+		<td align="center">89.4</td>
+		<td align="center">90.8</td>
+	</tr>
+	<tr>
+		<td align="center">GPQA-Diamond</td>
+		<td align="center">80.5</td>
+		<td align="center">82.3</td>
+		<td align="center">79.1</td>
+		<td align="center">77.1</td>
+		<td align="center">85.5</td>
+	</tr>
+	<tr>
+		<td align="center">LiveCodeBench v6</td>
+		<td align="center">81.4</td>
+		<td align="center">78.1</td>
+		<td align="center">80.7</td>
+		<td align="center">70.1</td>
+		<td align="center">80.7</td>
+	</tr>
+	<tr>
+		<td align="center">MMLU-Pro</td>
+		<td align="center">83.3</td>
+		<td align="center">83.3</td>
+		<td align="center">83.8</td>
+		<td align="center">83.8</td>
+		<td align="center">86.1</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Agentic Tool Use</i></td>
+	</tr>
+	<tr>
+		<td align="center">τ<sup>2</sup>-Bench (Retail)</td>
+		<td align="center">77.9</td>
+		<td align="center">78.3</td>
+		<td align="center">78.6</td>
+		<td align="center">67.0</td>
+		<td align="center">84.7</td>
+	</tr>
+	<tr>
+		<td align="center">τ<sup>2</sup>-Bench (Airline)</td>
+		<td align="center">56.5</td>
+		<td align="center">60.0</td>
+		<td align="center">60.4</td>
+		<td align="center">62.0</td>
+		<td align="center">67.5</td>
+	</tr>
+	<tr>
+		<td align="center">τ<sup>2</sup>-Bench (Telecom)</td>
+		<td align="center">73.0</td>
+		<td align="center">74.1</td>
+		<td align="center">73.5</td>
+		<td align="center">44.7</td>
+		<td align="center">99.3</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Instruction Following</i></td>
+	</tr>
+	<tr>
+		<td align="center">IFBench</td>
+		<td align="center">62.6</td>
+		<td align="center">74.0</td>
+		<td align="center">67.3</td>
+		<td align="center">59.2</td>
+		<td align="center">76.5</td>
+	</tr>
+	<tr>
+		<td align="center">IFEval</td>
+		<td align="center">89.6</td>
+		<td align="center">92.8</td>
+		<td align="center">89.7</td>
+		<td align="center">88.2</td>
+		<td align="center">95.0</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Long Context Understanding</i></td>
+	</tr>
+	<tr>
+		<td align="center">AA-LCR</td>
+		<td align="center">50.6</td>
+		<td align="center">68.0</td>
+		<td align="center">53.5</td>
+		<td align="center">58.7</td>
+		<td align="center">67.3</td>
+	</tr>
+	<tr>
+		<td align="center" colspan='7' style="background: linear-gradient(90deg, rgba(252,146,108,0.3) 0%, rgba(227,67,189,0.3) 50%, rgba(104,76,244,0.3) 100%); font-weight: bold; height:32px; padding-top:2px; padding-bottom:2px;"><i>Korean</i></td>
+	</tr>
+	<tr>
+		<td align="center">KMMLU-Pro</td>
+		<td align="center">67.6</td>
+		<td align="center">72.5</td>
+		<td align="center">67.3</td>
+		<td align="center">71.1</td>
+		<td align="center">73.0</td>
+	</tr>
+	<tr>
+		<td align="center">KoBALT</td>
+		<td align="center">52.1</td>
+		<td align="center">63.6</td>
+		<td align="center">61.8</td>
+		<td align="center">51.1</td>
+		<td align="center">54.9</td>
+	</tr>
+</table>
+
+
+
+The following table compares the benchmark results of EXAONE 4.5 between BF16 and AWQ precision.
+
+<table>
+    <tr>
+        <th>Quantization Type</th>
+        <th>MMMU</th>
+        <th>MMMU Pro</th>
+        <th>MathVista (mini)</th>
+        <th>MathVision</th>
+        <th>WeMath</th>
+        <th>LogicVista</th>
+        <th>Charxiv (RQ)</th>
+        <th>BLINK</th>
+        <th>K-Viscuit</th>
+        <th>KRETA</th>
+    </tr>
+    <tr>
+        <td align="center">BF16</td>
+        <td align="center">78.7</td>
+        <td align="center">68.6</td>
+        <td align="center">85.0</td>
+        <td align="center">75.2</td>
+        <td align="center">79.1</td>
+        <td align="center">73.8</td>
+        <td align="center">71.7</td>
+        <td align="center">68.8</td>
+        <td align="center">80.1</td>
+        <td align="center">91.9</td>
+    </tr>
+    <tr>
+        <td align="center">AWQ</td>
+        <td align="center">77.4</td>
+        <td align="center">66.8</td>
+        <td align="center">84.3</td>
+        <td align="center">71.8</td>
+        <td align="center">79.4</td>
+        <td align="center">72.0</td>
+        <td align="center">70.3</td>
+        <td align="center">68.0</td>
+        <td align="center">79.1</td>
+        <td align="center">90.5</td>
+    </tr>
+</table>
+
+
+
+## Quickstart
+
+
+### Serving EXAONE 4.5
+
+For better inference speed and memory usage, it is preferred to serve the model using optimized inference engines. The EXAONE 4.5 model is supported by various frameworks, including TensorRT-LLM, vLLM, SGLang, and llama.cpp. Support will be expanded in the future.
+
+Practically, you can serve the AWQ-quantized EXAONE 4.5 model with 256K context length on **single H200 GPU**, or **2x A100-40GB GPUs** by using a tensor-parallelism.
+
+
+### TensorRT-LLM
+
+TensorRT-LLM provides zero day support for EXAONE 4.5. Transformers library of our fork is required to utilize EXAONE 4.5 model.
+You can install Transformers by running the following commands:
+
+```bash
+pip install git+https://github.com/nuxlear/transformers.git@add-exaone4_5
+```
+
+Please refer to the official [installation guide](https://github.com/NVIDIA/TensorRT-LLM?tab=readme-ov-file#getting-started), and [EXAONE documentations](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/models/core/exaone), and [EXAONE 4.5 PR](https://github.com/NVIDIA/TensorRT-LLM/pull/12873) for the detail.
+
+After you install the TensorRT-LLM, you can launch the server with the following code snippet. You can remove unnecessary arguments from the snippet.
+
+```bash
+trtllm-serve LGAI-EXAONE/EXAONE-4.5-33B-AWQ \
+    —tp_size 2 \
+    —port 8000 \
+    —reasoning_parser qwen3
+
+```
+
+An OpenAI-compatible API server will be available at http://localhost:8000/v1.
+
+
+### vLLM
+
+Both Transformers and vLLM of our forks are required to utilize EXAONE 4.5 model.
+You can install the requirements by running the following commands:
+
+```bash
+uv pip install git+https://github.com/lkm2835/vllm.git@add-exaone4_5
+uv pip install git+https://github.com/nuxlear/transformers.git@add-exaone4_5
+```
+
+After you install the vLLM, you can launch the server with the following code snippet. You can remove unnecessary arguments from the snippet.
+
+```bash
+vllm serve LGAI-EXAONE/EXAONE-4.5-33B-AWQ \
+    --served-model-name EXAONE-4.5-33B-AWQ \
+    --port 8000 \
+    --tensor-parallel-size 2 \
+    --max-model-len 262144 \
+    --reasoning-parser qwen3 \
+    --enable-auto-tool-choice \
+    --tool-call-parser hermes \
+    --limit-mm-per-prompt '{"image": 64}' \
+    --speculative_config '{
+        "method": "mtp", 
+        "num_speculative_tokens": 3
+    }'
+
+```
+
+An OpenAI-compatible API server will be available at http://localhost:8000/v1.
+
+
+### SGLang
+
+Both Transformers and SGLang of our forks are required to utilize EXAONE 4.5 model. 
+You can install the requirements by running the following commands:
+
+```bash
+uv pip install 'git+https://github.com/lkm2835/sglang.git@add-exaone4_5#subdirectory=python&egg=sglang[all]'
+uv pip install git+https://github.com/nuxlear/transformers.git@add-exaone4_5
+```
+
+After you install the SGLang, you can launch the server with the following code snippet. You can remove unnecessary arguments from the snippet.
+
+```bash
+python -m sglang.launch_server \
+    --model-path LGAI-EXAONE/EXAONE-4.5-33B-AWQ \
+    --served-model-name EXAONE-4.5-33B-AWQ \
+    --port 8000 \
+    --tp-size 2 \
+    --mem-frac 0.81 \
+    --reasoning-parser qwen3 \
+    --tool-call-parser hermes \
+    --speculative-algorithm EAGLE \
+    --speculative-num-steps 3 \
+    --speculative-eagle-topk 1 \
+    --speculative-num-draft-tokens 4
+
+```
+
+An OpenAI-compatible API server will be available at http://localhost:8000/v1.
+
+
+
+### Using EXAONE 4.5
+
+After launching the OpenAI-compatible server with EXAONE 4.5, you can seamlessly use the model via API with a single code integration, even though the serving framework has changed. To use OpenAI Python SDK and following examples, you should install the `openai` library on your environment.
+
+
+> [!IMPORTANT]
+> To achieve the expected performance, we recommend using the following configurations:
+> - We recommend to use `temperature=1.0`, `top_p=0.95`, `presence_penalty=1.5` for general purpose.
+> - We recommend to use `temperature=0.6`, `top_p=0.95`, `presence_penalty=1.5`, `top_k=20` for OCR/document-related tasks, and Korean inputs.
+> - We recommend to use `temperature=1.0`, `top_p=0.95` for text-only inputs.
+> - Different from EXAONE-4.0, EXAONE 4.5 uses `enable_thinking=True` as default. Thus, you need to set `enable_thinking=False` when you want to use non-reasoning mode.
+> - EXAONE 4.5 prefers using `\boxed{}` format to answer the question. We recommend using this format with the corresponding format instruction for better parsing accuracy. 
+>
+
+
+
+You can easily try model's chat completions by using OpenAI Python SDK. For your server in local machine, you will need to change your `base_url` and `api_key` for the OpenAI client.
+
+
+### Image-Text QA
+
+#### Reasoning mode
+
+For tasks that require accurate results, you can run the EXAONE 4.5 model in reasoning mode as follows.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8000/v1",
+    api_key="EMPTY",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": "https://github.com/Aim-Highest/EXAONE-4.5/blob/main/assets/exaone45_input2.png?raw=true",
+                },
+            },
+            {
+                "type": "text",
+                "text": "How much larger is the model released in winter 2025 compared with the one released in summer 2024?",
+            },
+        ]
+    }
+]
+
+response = client.chat.completions.create(
+    model="EXAONE-4.5-33B-AWQ",
+    messages=messages,
+    max_tokens=32768,
+    temperature=1.0,
+    top_p=0.95,
+    presence_penalty=1.5,
+    extra_body={
+        "chat_template_kwargs": {
+            "enable_thinking": True,  # default: True
+        }
+    }, 
+)
+print(response)
+```
+
+#### Non-reasoning mode
+
+For tasks where latency matters more than accuracy, you can run the EXAONE 4.5 model in non-reasoning mode as follows.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8000/v1",
+    api_key="EMPTY",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": "https://github.com/Aim-Highest/EXAONE-4.5/blob/main/assets/exaone45_input1.jpg?raw=true",
+                },
+            },
+            {
+                "type": "text",
+                "text": "What dish is the person preparing, and how is it made?",
+            },
+        ]
+    }
+]
+
+response = client.chat.completions.create(
+    model="EXAONE-4.5-33B-AWQ",
+    messages=messages,
+    max_tokens=32768,
+    temperature=1.0,
+    top_p=0.95,
+    presence_penalty=1.5,
+    extra_body={
+        "chat_template_kwargs": {
+            "enable_thinking": False,  # default: True
+        }
+    }, 
+)
+print(response)
+
+```
+
+
+### Text-only QA
+
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8000/v1",
+    api_key="EMPTY",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Explain how useful you are.",
+    }
+]
+
+response = client.chat.completions.create(
+    model="EXAONE-4.5-33B-AWQ",
+    messages=messages,
+    max_tokens=32768,
+    temperature=1.0,
+    top_p=0.95,
+    extra_body={
+        "chat_template_kwargs": {
+            "enable_thinking": True,  # default: True
+        }
+    }, 
+)
+print(response)
+
+```
+
+
+### Agentic Use
+
+The following example demonstrates the agentic capability of EXAONE 4.5 for image-text inputs. You can use your own agents, skills, or other harnesses with the EXAONE 4.5 model.
+
+```python
+# If needed:
+# pip install langchain langchain-openai langchain-mcp-adapters
+# curl -LsSf https://astral.sh/uv/install.sh | sh
+# sudo apt-get update && sudo apt-get install -y nodejs npm
+
+import os
+import asyncio
+from langchain_openai import ChatOpenAI
+from langchain.agents import create_agent
+from langchain_mcp_adapters.client import MultiServerMCPClient
+
+def print_message(msg):
+    parts = msg.content if isinstance(msg.content, list) else [{"type": "text", "text": msg.content or ""}]
+    text_out, reasoning_out = [], []
+
+    for p in parts:
+        if isinstance(p, dict):
+            if p.get("type") in ("text", "output_text") and p.get("text"):
+                text_out.append(p["text"])
+            elif p.get("type") in ("reasoning", "reasoning_text") and p.get("text"):
+                reasoning_out.append(p["text"])
+
+    if reasoning_out:
+        print("\n[assistant_reasoning_content]")
+        print("\n".join(reasoning_out))
+    if text_out:
+        print("\n[assistant_content]")
+        print("\n".join(text_out))
+
+async def main():
+    model = ChatOpenAI(
+        model="EXAONE-4.5-33B-AWQ",
+        base_url="http://localhost:8000/v1",
+        api_key="EMPTY",
+        temperature=1.0,
+        model_kwargs={"top_p": 0.95},
+    )
+
+    client = MultiServerMCPClient({
+        "filesystem": {
+            "transport": "stdio",
+            "command": "npx",
+            "args": ["-y", "@modelcontextprotocol/server-filesystem", "/tmp"],
+        },
+        "fetch": {
+            "transport": "stdio",
+            "command": "uvx",
+            "args": ["mcp-server-fetch"],
+        },
+        "duckduckgo": {
+            "transport": "stdio",
+            "command": "uvx",
+            "args": ["duckduckgo-mcp-server"],
+        },
+    })
+
+    agent = create_agent(model, await client.get_tools())
+
+    inputs = {
+        "messages": [{
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": (
+                        "Look at the image and identify the landmark. "
+                        "Use the DuckDuckGo MCP tool to verify its name, height, and location. "
+                        "Then use the fetch tool to read a fuller article page about it. "
+                        "Create /tmp/mcp-demo and write a short markdown file to "
+                        "/tmp/mcp-demo/landmark.md with: name, location, height, and a one-sentence summary of the article. "
+                        "Finally, return only the exact file content."
+                    ),
+                },
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": "https://upload.wikimedia.org/wikipedia/commons/a/a8/Tour_Eiffel_Wikimedia_Commons.jpg"
+                    },
+                },
+            ],
+        }]
+    }
+
+    async for step in agent.astream(inputs, stream_mode="values"):
+        msg = step["messages"][-1]
+        if getattr(msg, "type", "") == "ai":
+            print_message(msg)
+            for tc in getattr(msg, "tool_calls", []) or []:
+                print(f"\n[tool call] {tc['name']}({tc['args']})")
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
+```
+
+
+
+
+
+## Limitation
+
+EXAONE 4.5 models, like all existing multimodal models, have certain limitations and may occasionally generate
+inappropriate responses. The multimodal model generates responses based on the output probability of tokens, and it
+is determined during learning from training data. While we make 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 4.5 models 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 models do 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 4.5 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 4.5 models.
+
+
+
+## License
+
+The model is licensed under [EXAONE AI Model License Agreement 1.2 - NC](./LICENSE)
+
+
+
+## Citation
+
+```
+@article{exaone-4.5,
+  title={EXAONE 4.5 Technical Report},
+  author={{LG AI Research}},
+  journal={arXiv preprint arXiv:2604.08644},
+  year={2026}
+}
+```
+
+
+## Contact
+
+LG AI Research Technical Support: contact_us@lgresearch.ai
+

chat_template.jinja

@@ -0,0 +1,161 @@
+{% set image_count = namespace(value=0) %}
+{% set video_count = namespace(value=0) %}
+
+{%- set role_indicators = {
+    'user': '<|user|>\n',
+    'assistant': '<|assistant|>\n',
+    'system': '<|system|>\n',
+    'tool': '<|tool|>\n',
+    'tool_declare': '<|tool_declare|>\n'
+} %}
+{%- set end_of_turn = '<|endofturn|>\n' %}
+
+
+{%- macro declare_available_tools(tools) %}
+    {{- "# Tools" }}
+    {{- "\n" }}
+    {%- for tool in tools %}
+        {{- "<tool>" }}
+        {{- tool | tojson(ensure_ascii=False) | safe }}
+        {{- "</tool>\n" }}
+    {%- endfor %}
+{%- endmacro %}
+
+
+{%- set ns = namespace(last_query_index = messages|length - 1, last_query_index_not_yet_determined = true) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.last_query_index_not_yet_determined and message.role == "user" and message.content is string %}
+        {%- set ns.last_query_index = index -%}
+        {%- set ns.last_query_index_not_yet_determined = false -%}
+    {%- endif %}
+{%- endfor %}
+
+{%- if tools is defined and tools %}
+    {{- role_indicators['tool_declare'] }}
+    {{- declare_available_tools(tools) }}
+    {{- end_of_turn -}}  
+{%- endif %}
+
+{%- for i in range(messages | length) %}
+    {%- set msg = messages[i] %}
+    {%- set role = msg.role %}
+    {%- if role not in role_indicators %}
+        {{- raise_exception('Unknown role: ' ~ role) }}
+    {%- endif %}
+    
+    {%- if i == 0 %}
+        {%- if role == 'system' %}
+            {{- role_indicators['system'] }}
+            {{- msg.content }}
+            {{- end_of_turn -}}
+            {%- continue %}          
+        {%- endif %}
+    {%- endif %}
+
+    {%- if role == 'assistant' %}
+        {{- role_indicators['assistant'] }}
+
+        {%- set content = (msg.content if (msg.content is defined and msg.content) else "") -%}
+        {%- set reasoning = none -%}
+
+        {%- if msg.reasoning_content is defined and msg.reasoning_content%}
+            {%- set reasoning = msg.reasoning_content.strip() -%}
+        {%- elif content and "</think>" in content %}
+            {%- set _parts = content.split('</think>') -%}
+            {%- set reasoning = _parts[0].lstrip('<think>').strip() -%}
+            {%- set content = _parts[-1].strip() -%}
+        {%- endif %}
+
+        {%- if not (reasoning and i > ns.last_query_index) or (skip_think is defined and skip_think) %}
+            {%- set reasoning = none %}
+        {%- endif %}
+
+        {%- set content = content.strip() -%}
+        
+        {{- "<think>\n" }}
+        {{- (reasoning if reasoning is not none else "") }}
+        {{- "\n</think>\n\n" }}
+
+        {{- content }}
+
+        {%- if msg.tool_calls %}
+            {%- if content is defined and content %}
+                {{- "\n" }}
+            {%- endif %}
+            {%- for tool_call in msg.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+
+                {%- if tool_call.arguments is defined %}
+                    {%- set arguments = tool_call.arguments %}
+                {%- elif tool_call.parameters is defined %}
+                    {%- set arguments = tool_call.parameters %}
+                {%- else %}
+                    {{- raise_exception('arguments or parameters are mandatory: ' ~ tool_call) }}
+                {%- endif %}
+                {%- if arguments is string %}
+                    {{- "<tool_call>" }}{"name": "{{- tool_call.name }}", "arguments": {{ arguments }}}{{- "</tool_call>" }}
+                {%- else %}
+                    {{- "<tool_call>" }}{"name": "{{- tool_call.name }}", "arguments": {{ arguments | tojson(ensure_ascii=False) | safe }}}{{- "</tool_call>" }}
+                {%- endif %}
+                {%- if not loop.last %}
+                    {{- "\n" }}
+                {%- endif %}
+
+            {%- endfor %}
+        {%- endif %}
+        {{- end_of_turn -}}
+
+    {%- elif role == "tool" %}
+        {%- if i == 0 or messages[i - 1].role != "tool" %}
+            {{- role_indicators['tool'] }}
+        {%- endif %}
+        {%- if msg.content is defined %}          
+            {%- if msg.content is string %}  
+                {{- "<tool_result>" }}{{ msg.content }}{{- "</tool_result>" }} 
+            {%- else %}
+                {{- "<tool_result>" }}{{ msg.content | tojson(ensure_ascii=False) | safe }}{{- "</tool_result>" }}            
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.last or messages[i + 1].role != "tool" %}
+            {{- end_of_turn -}}
+        {%- else %}
+            {{- "\n" }}
+        {%- endif %}
+
+    {%- else %}
+        {{- role_indicators[role] }}
+        {%- if msg.content is string %}
+            {{- msg.content }}
+        {%- else %}
+            {%- for content in msg.content %} 
+                {%- if content.type == 'image' %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                    {%- if add_vision_id %}Picture {{ image_count.value }}: {% endif %}
+                    {{- "<vision><|image_pad|></vision>\n" }}
+                {%- elif content.type == 'video' %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                    {%- if add_vision_id %}Video {{ video_count.value }}: {% endif %}
+                    {{- "<vision><|video_pad|></vision>\n" }}
+                {%- elif content.type == 'text' %}
+                    {{- content.text }}
+                {%- else %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        {{- end_of_turn -}}
+    {%- endif %}
+{% endfor %}
+
+
+{%- if add_generation_prompt %}
+    {{- role_indicators['assistant'] }}
+    {%- if enable_thinking is not defined or enable_thinking is true %}
+        {{- "<think>\n" }}
+    {%- else %}
+        {{- "<think>\n\n</think>\n\n" }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,329 @@
+{
+  "architectures": [
+    "Exaone4_5_ForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 67,
+  "model_type": "exaone4_5",
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "pack-quantized",
+        "input_activations": null,
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": 128,
+          "num_bits": 4,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "group",
+          "symmetric": true,
+          "type": "int",
+          "zp_dtype": null
+        }
+      }
+    },
+    "format": "pack-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "model.visual.blocks.0.attn.proj",
+      "model.visual.blocks.0.attn.qkv",
+      "model.visual.blocks.0.mlp.gate_proj",
+      "model.visual.blocks.0.mlp.up_proj",
+      "model.visual.blocks.0.mlp.down_proj",
+      "model.visual.blocks.1.attn.proj",
+      "model.visual.blocks.1.attn.qkv",
+      "model.visual.blocks.1.mlp.gate_proj",
+      "model.visual.blocks.1.mlp.up_proj",
+      "model.visual.blocks.1.mlp.down_proj",
+      "model.visual.blocks.2.attn.proj",
+      "model.visual.blocks.2.attn.qkv",
+      "model.visual.blocks.2.mlp.gate_proj",
+      "model.visual.blocks.2.mlp.up_proj",
+      "model.visual.blocks.2.mlp.down_proj",
+      "model.visual.blocks.3.attn.proj",
+      "model.visual.blocks.3.attn.qkv",
+      "model.visual.blocks.3.mlp.gate_proj",
+      "model.visual.blocks.3.mlp.up_proj",
+      "model.visual.blocks.3.mlp.down_proj",
+      "model.visual.blocks.4.attn.proj",
+      "model.visual.blocks.4.attn.qkv",
+      "model.visual.blocks.4.mlp.gate_proj",
+      "model.visual.blocks.4.mlp.up_proj",
+      "model.visual.blocks.4.mlp.down_proj",
+      "model.visual.blocks.5.attn.proj",
+      "model.visual.blocks.5.attn.qkv",
+      "model.visual.blocks.5.mlp.gate_proj",
+      "model.visual.blocks.5.mlp.up_proj",
+      "model.visual.blocks.5.mlp.down_proj",
+      "model.visual.blocks.6.attn.proj",
+      "model.visual.blocks.6.attn.qkv",
+      "model.visual.blocks.6.mlp.gate_proj",
+      "model.visual.blocks.6.mlp.up_proj",
+      "model.visual.blocks.6.mlp.down_proj",
+      "model.visual.blocks.7.attn.proj",
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+      "model.visual.blocks.7.mlp.gate_proj",
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+      "model.visual.blocks.8.attn.qkv",
+      "model.visual.blocks.8.mlp.gate_proj",
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+      "model.visual.blocks.14.mlp.gate_proj",
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+      "model.visual.blocks.16.mlp.gate_proj",
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+      "model.visual.blocks.17.attn.qkv",
+      "model.visual.blocks.17.mlp.gate_proj",
+      "model.visual.blocks.17.mlp.up_proj",
+      "model.visual.blocks.17.mlp.down_proj",
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+      "model.visual.blocks.18.attn.qkv",
+      "model.visual.blocks.18.mlp.gate_proj",
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+      "model.visual.blocks.19.attn.qkv",
+      "model.visual.blocks.19.mlp.gate_proj",
+      "model.visual.blocks.19.mlp.up_proj",
+      "model.visual.blocks.19.mlp.down_proj",
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+      "model.visual.blocks.20.attn.qkv",
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+      "model.visual.blocks.27.mlp.down_proj",
+      "model.visual.merger.mlp.0",
+      "model.visual.merger.mlp.2",
+      "lm_head",
+      "mtp.fc",
+      "mtp.layers.0.self_attn.q_proj",
+      "mtp.layers.0.self_attn.k_proj",
+      "mtp.layers.0.self_attn.v_proj",
+      "mtp.layers.0.self_attn.o_proj",
+      "mtp.layers.0.mlp.gate_proj",
+      "mtp.layers.0.mlp.up_proj",
+      "mtp.layers.0.mlp.down_proj"
+    ],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed",
+    "sparsity_config": {},
+    "transform_config": {},
+    "version": "0.15.1.a20260414"
+  },
+  "text_config": {
+    "_num_mtp_layers": 1,
+    "architectures": [
+      "Exaone4ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
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+    "hidden_act": "silu",
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+    "initializer_range": 0.02,
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+      "full_attention",
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+      "sliding_attention",
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+      "full_attention",
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+      "sliding_attention",
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+      "full_attention",
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+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
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+      "sliding_attention",
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+      "full_attention",
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+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 262144,
+    "model_type": "exaone4",
+    "mtp_loss_scaling_factor": 0.05,
+    "mtp_share_layers": true,
+    "num_attention_heads": 40,
+    "num_hidden_layers": 64,
+    "num_key_value_heads": 8,
+    "num_nextn_predict_layers": 1,
+    "pad_token_id": 0,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+      "factor": 16.0,
+      "high_freq_factor": 4.0,
+      "low_freq_factor": 1.0,
+      "original_max_position_embeddings": 8192,
+      "rope_theta": 1000000.0,
+      "rope_type": "llama3"
+    },
+    "sliding_window": 4096,
+    "sliding_window_pattern": "LLLG",
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 153600
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.6.0.dev0",
+  "video_token_id": 68,
+  "vision_config": {
+    "depth": 28,
+    "dtype": "bfloat16",
+    "fullatt_block_indexes": [
+      6,
+      13,
+      20,
+      27
+    ],
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "in_channels": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 5120,
+    "model_type": "exaone4_5_vision",
+    "num_heads": 32,
+    "num_key_value_heads": 8,
+    "out_hidden_size": 5120,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "temporal_patch_size": 2,
+    "tokens_per_second": 2,
+    "window_size": 112
+  },
+  "vision_end_token_id": 74,
+  "vision_start_token_id": 73,
+  "vision_token_id": 67,
+  "vocab_size": 153600
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 53,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 0,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "transformers_version": "5.6.0.dev0",
+  "use_cache": true
+}

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preprocessor_config.json

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+{
+  "crop_size": null,
+  "data_format": "channels_first",
+  "default_to_square": true,
+  "device": null,
+  "do_center_crop": null,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
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+}

processor_config.json

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+{
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+    "data_format": "channels_first",
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+    "device": null,
+    "do_center_crop": null,
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+      0.40821073
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+  "video_processor": {
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+    "do_convert_rgb": true,
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+    "do_sample_frames": false,
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+    "video_processor_type": "Exaone4_5_VideoProcessor"
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+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0bd798efa30739e209d51f36cfc2f0a636711e37ad9d69b77e4e5c8ca5f09fab
+size 12160205

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "[BOS]",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endofturn|>",
+  "errors": "replace",
+  "is_local": true,
+  "local_files_only": false,
+  "model_input_names": [
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+    "attention_mask"
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+  "model_specific_special_tokens": {},
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+  "padding_side": "right",
+  "processor_class": "Exaone4_5_Processor",
+  "split_special_tokens": false,
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "[UNK]"
+}