Refactor lora

docs/en/llm/api_server_lora.md

@@ -1,8 +1,8 @@
 # Serving LoRA
 
-## Launch S-LoRA
+## Launch LoRA
 
-S-LoRA is currently only supported by the PyTorch backend. Its deployment process is similar to that of other models, and you can view the commands using lmdeploy `serve api_server -h`. Among the parameters supported by the PyTorch backend, there are configuration options for S-LoRA.
+LoRA is currently only supported by the PyTorch backend. Its deployment process is similar to that of other models, and you can view the commands using lmdeploy `serve api_server -h`. Among the parameters supported by the PyTorch backend, there are configuration options for LoRA.
 
 ```txt
 PyTorch engine arguments:

docs/en/llm/pipeline.md

@@ -163,7 +163,7 @@ response = pipe(prompts, gen_config=gen_config)
 print(response)
 ```
 
-- **An example for slora.**
+- **An example for lora.**
 
 ```python
 from lmdeploy import pipeline, GenerationConfig, PytorchEngineConfig

docs/zh_cn/llm/api_server_lora.md

@@ -1,8 +1,8 @@
 # LoRA 推理服务
 
-## 启动 S-LoRA 服务
+## 启动 LoRA 服务
 
-S-LoRA 目前只有 pytorch 后端支持。它的服务化，和其他模型服务化一样，命令都可以用 `lmdeploy serve api_server -h` 查看。其中 pytorch 后端支持的参数就有 S-LoRA 的配置内容。
+LoRA 目前只有 pytorch 后端支持。它的服务化，和其他模型服务化一样，命令都可以用 `lmdeploy serve api_server -h` 查看。其中 pytorch 后端支持的参数就有 LoRA 的配置内容。
 
 ```txt
 PyTorch engine arguments:

docs/zh_cn/llm/pipeline.md

@@ -165,7 +165,7 @@ response = pipe(prompts, gen_config=gen_config)
 print(response)
 ```
 
-- **一个 slora 的例子**
+- **一个 lora 的例子**
 
 ```python
 from lmdeploy import pipeline, GenerationConfig, PytorchEngineConfig