Update README.md

README.md

@@ -34,19 +34,19 @@ The EXAONE 4.0 model series consists of two sizes: a mid-size **32B** model opti
 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.
+2. **QK-Reorder-Norm**: We reorder the LayerNorm position from the traditional Pre-LN scheme by applying LayerNorm directly to the attention and MLP outputs, and we add RMS normalization right after the Q and K projection. It helps yield better performance on downstream tasks despite consuming more computation.
 
 For more details, please refer to our [technical report](https://arxiv.org/abs/2507.11407), [blog](https://www.lgresearch.ai/blog/view?seq=576), 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]]
+- Number of Parameters (without embeddings): 1.07B
+- Number of Layers: 30
+- Number of Attention Heads: GQA with 32-heads and 8-KV heads
 - Vocab Size: 102,400
-- Context Length: [[context_length]] tokens
-[[quantization]]
+- Context Length: 65,536 tokens
+- Quantization: `Q8_0`, `Q6_K`, `Q5_K_M`, `Q4_K_M`, `IQ4_XS` in GGUF format (also includes `BF16` weights)
 
 ## Quickstart