diff --git a/doc/en/usage.md b/doc/en/usage.md
index 864ead6..0fa4bf5 100644
--- a/doc/en/usage.md
+++ b/doc/en/usage.md
@@ -385,3 +385,11 @@ Taking the configuration of the demo training on a single machine with 8 GPUs on
 2023-07-07 12:29:13,147	INFO train.py:323 in record_current_batch_training_metrics -- tflops=189.65918563194305,step=4,loss=10.149517059326172,tgs (tokens/gpu/second)=4270.52,lr=1.2000000000000002e-06,loss_scale=65536.0,grad_norm=51.582841631508145,micro_num=4,num_consumed_tokens=655360,inf_nan_skip_batches=0,num_samples_in_batch=19,largest_length=2048,largest_batch=6,smallest_batch=3,adam_beta2=0.95,fwd_bwd_time=3.68
 2023-07-07 12:29:16,994	INFO train.py:323 in record_current_batch_training_metrics -- tflops=189.3109313713174,step=5,loss=9.822169303894043,tgs (tokens/gpu/second)=4262.67,lr=1.4000000000000001e-06,loss_scale=65536.0,grad_norm=47.10386835560855,micro_num=4,num_consumed_tokens=786432,inf_nan_skip_batches=0,num_samples_in_batch=17,largest_length=2048,largest_batch=6,smallest_batch=3,adam_beta2=0.95,fwd_bwd_time=3.69
 ```
+
+### Long Text Generation
+
+During the inference phase, you can turn on the Dynamic NTK option of RoPE by setting `use_dynamic_ntk_rope=True` in the model configuration, so that the model can adapt to long text input and output and achieve an extrapolation effect of 16K.
+Regarding the principle of Dyanmic NTK, please refer to
+
+1. https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases
+2. https://kexue.fm/archives/9675
diff --git a/doc/usage.md b/doc/usage.md
index 82c20e0..22af249 100644
--- a/doc/usage.md
+++ b/doc/usage.md
@@ -368,3 +368,11 @@ $ torchrun --nnodes=1 --nproc_per_node=8 train.py --config ./configs/7B_sft.py -
 2023-07-07 12:29:13,147	INFO train.py:323 in record_current_batch_training_metrics -- tflops=189.65918563194305,step=4,loss=10.149517059326172,tgs (tokens/gpu/second)=4270.52,lr=1.2000000000000002e-06,loss_scale=65536.0,grad_norm=51.582841631508145,micro_num=4,num_consumed_tokens=655360,inf_nan_skip_batches=0,num_samples_in_batch=19,largest_length=2048,largest_batch=6,smallest_batch=3,adam_beta2=0.95,fwd_bwd_time=3.68
 2023-07-07 12:29:16,994	INFO train.py:323 in record_current_batch_training_metrics -- tflops=189.3109313713174,step=5,loss=9.822169303894043,tgs (tokens/gpu/second)=4262.67,lr=1.4000000000000001e-06,loss_scale=65536.0,grad_norm=47.10386835560855,micro_num=4,num_consumed_tokens=786432,inf_nan_skip_batches=0,num_samples_in_batch=17,largest_length=2048,largest_batch=6,smallest_batch=3,adam_beta2=0.95,fwd_bwd_time=3.69
 ```
+
+### 长文本生成
+
+在推理阶段，您可以在模型配置中通过设置 `use_dynamic_ntk_rope=True` 开启 RoPE 的 Dynamic NTK 选项，从而使得模型适应长文本输入输出，达到 16K 的外推效果。
+关于 Dyanmic NTK 的原理，详细请参考
+
+1. https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases
+2. https://kexue.fm/archives/9675