Merge pull request #816 from KPCOFGS/main

Update README.md

README.md

@@ -321,7 +321,7 @@ For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy
 
 ### 6.4 Inference with TRT-LLM (recommended)
 
-[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3. 
+[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/deepseek_v3. 
 
 
 ### 6.5 Inference with vLLM (recommended)

inference/model.py

@@ -392,7 +392,7 @@ def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
 
 class MLA(nn.Module):
     """
-    Multi-Headed Attention Layer (MLA).
+    Multi-Head Latent Attention (MLA) Layer.
 
     Attributes:
         dim (int): Dimensionality of the input features.
@@ -442,7 +442,7 @@ class MLA(nn.Module):
 
     def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
         """
-        Forward pass for the Multi-Headed Attention Layer (MLA).
+        Forward pass for the Multi-Head Latent Attention (MLA) Layer.
 
         Args:
             x (torch.Tensor): Input tensor of shape (batch_size, seq_len, dim).