Update README.md

README.md

@@ -90,7 +90,7 @@ print("thinking content:", thinking_content)
 print("content:", content)
 ```
 
-For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.4` or  to create an OpenAI-compatible API endpoint:
+For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
 - SGLang:
     ```shell
     python -m sglang.launch_server --model-path Qwen/Qwen3-32B --reasoning-parser qwen3
@@ -100,7 +100,7 @@ For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.4` or  to create
     vllm serve Qwen/Qwen3-32B --enable-reasoning --reasoning-parser deepseek_r1
     ```
 
-For local use, applications such as llama.cpp, Ollama, LMStudio, and MLX-LM have also supported Qwen3.
+For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
 
 ## Switching Between Thinking and Non-Thinking Mode
 
@@ -274,7 +274,7 @@ YaRN is currently supported by several inference frameworks, e.g., `transformers
     {
         ...,
         "rope_scaling": {
-            "type": "yarn",
+            "rope_type": "yarn",
             "factor": 4.0,
             "original_max_position_embeddings": 32768
         }
@@ -286,12 +286,12 @@ YaRN is currently supported by several inference frameworks, e.g., `transformers
 
   For `vllm`, you can use
     ```shell
-    vllm serve ... --rope-scaling '{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072  
+    vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072  
     ```
 
   For `sglang`, you can use
     ```shell
-    python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
+    python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
     ```
 
   For `llama-server` from `llama.cpp`, you can use