We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. For more details, please refer to our technical report, blog and GitHub. This repository contains the instruction-tuned 2.4B language model with the following features:
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We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. For more details, please refer to our technical report, blog and GitHub. This repository contains the instruction-tuned 32B language model with the following features:
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We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. For more details, please refer to our technical report, blog and GitHub. This repository contains the AWQ-quantized weights of the instruction-tuned 32B language model with the following features:
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We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. For more details, please refer to our technical report, blog and GitHub. This repository contains the instruction-tuned 7.8B language model with the following features:
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We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. For more details, please refer to our technical report, blog and GitHub. This repository contains the AWQ-quantized weights of the instruction-tuned 7.8B language model with the following features:
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🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗 ✈️ Try on FriendliAI (licensed under commercial purposes) 📢 EXAONE 4.0 is officially supported by HuggingFace transformers! Please check out the guide below We introduce **EXAONE 4.0**, which integrates a **Non-reasoning mode** and **Reasoning mode** to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean. The EXAONE 4.0 model series consists of two sizes: a mid-size **32B** model optimized for high performance, and a small-size **1.2B** model designed for on-device applications.
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🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗 ✈️ Try on FriendliAI (licensed under commercial purposes) 📢 EXAONE 4.0 is officially supported by llama.cpp! Please check the guide below We introduce **EXAONE 4.0**, which integrates a **Non-reasoning mode** and **Reasoning mode** to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean. The EXAONE 4.0 model series consists of two sizes: a mid-size **32B** model optimized for high performance, and a small-size **1.2B** model designed for on-device applications.
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🎉 License Updated! We are pleased to announce our more flexible licensing terms 🤗 ✈️ Try on FriendliAI (licensed under commercial purposes) 📢 EXAONE 4.0 is officially supported by HuggingFace transformers! Please check out the guide below We introduce **EXAONE 4.0**, which integrates a **Non-reasoning mode** and **Reasoning mode** to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean. The EXAONE 4.0 model series consists of two sizes: a mid-size **32B** model optimized for high performance, and a small-size **1.2B** model designed for on-device applications.
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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, blog and GitHub. - 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: 4096 - 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)
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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. 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. EXAONE 4.5 33B (Reasoning) GPT-5 mini (Reasoning: high) Qwen3-VL 32B Thinking Qwen3-VL 235B Thinking Qwen3.5 27B (Reasoning)
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We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research. Evaluation results show that 1) EXAONE Deep **2.4B** outperforms other models of comparable size, 2) EXAONE Deep **7.8B** outperforms not only open-weight models of comparable scale but also a proprietary reasoning model OpenAI o1-mini, and 3) EXAONE Deep **32B** demonstrates competitive performance against leading open-weight models. > ### Note > The EXAONE Deep models are trained with an optimized configuration, > so we recommend following the Usage Guideline section to achieve optimal performance. The following table shows the evaluation results of reasoning tasks such as math and coding. The full evaluation results can be found in the documentation.
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