Model overview
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)
Key features
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Limitations & considerations
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Public benchmarks & run configuration
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Sources & traceability
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