diff --git a/vllm/README.md b/vllm/README.md
index f81ab0c..1087638 100644
--- a/vllm/README.md
+++ b/vllm/README.md
@@ -23,6 +23,7 @@ llm-scaler-vllm is an extended and optimized version of vLLM, specifically adapt
    2.7 [Finding maximum Context Length](#27-finding-maximum-context-length)   
    2.8 [Multi-Modal Webui](#28-multi-modal-webui)  
    2.9 [Multi-node Distributed Deployment (PP/TP)](#29-multi-node-distributed-deployment-pptp)
+   2.10 [BPE-Qwen Tokenizer](#210-bpe-qwen-tokenizer)
 4. [Supported Models](#3-supported-models)  
 5. [Troubleshooting](#4-troubleshooting)
 6. [Performance tuning](#5-performance-tuning)
@@ -2036,6 +2037,7 @@ To use fp8 online quantization, simply replace `--quantization sym_int4` with:
 ```
 
 For those models that have been quantized before, such as AWQ-Int4/GPTQ-Int4/FP8 models, user do not need to specify the `--quantization` option.
+
 ---
 
 ### 2.3 Embedding and Reranker Model Support
@@ -2065,6 +2067,7 @@ python3 -m vllm.entrypoints.openai.api_server \
 
 ---
 After starting the vLLM service, you can follow this link to use it.
+
 #### [Embedding api](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#embeddings-api_1)
 
 ```bash
@@ -2179,6 +2182,7 @@ curl http://localhost:8000/v1/chat/completions \
 ```
 
 if want to process image in server local, you can `"url": "file:/llm/models/test/1.jpg"` to test.
+
 ---
 
 ### 2.4.1 Audio Model Support [Deprecated]
@@ -2228,52 +2232,39 @@ curl http://localhost:8000/v1/audio/transcriptions \
 
 ### 2.4.2 dots.ocr Support
 
-Git clone the repo:
+To launch `dots.ocr`, follow the instructions in [1.4 Launching the Serving Service](#14-launching-the-serving-service), specifying the dots.ocr model, setting the model path to `/llm/models/dots.ocr`, the served-model-name to `model`, and the port to 8000.
 
-```bash
-https://github.com/rednote-hilab/dots.ocr.git
-cd dots.ocr
-```
 
-Then, we should comment out the following two items in `requirements.txt`:
+Once the service is running, you can use the method provided in the `dots.ocr` repository to launch Gradio for testing.
 
-> flash-attn==2.8.0.post2 and accelerate  # because these two dependencies will require cuda
+---
 
-After commenting out these two elements, we can install the dependencies:
+#### Clone the repository
 
 ```bash
-# Assuming you have installed torch/ipex etc.
-pip install --no-deps accelerate
-pip install -e .
+git clone https://github.com/rednote-hilab/dots.ocr.git
+cd dots.ocr
 ```
 
-To download model weights in `dots.ocr`:
-```bash
-# In dots.ocr
-python3 tools/download_model.py
+#### Modify dependencies
 
-# with modelscope
-python3 tools/download_model.py --type modelscope
+Comment out the following two lines in `requirements.txt`:
+
+```
+flash-attn==2.8.0.post2
+transformers  # These two dependencies can cause conflicts
 ```
 
-In order to run dots.ocr, we will need to change codes in `./weights/DotsOCR`:
+Then install the dependencies:
 
 ```bash
-cd ./weights/DotsOCR
-patch -p1 < YOUR_PATH/dots_ocr.patch
+pip install -e .
 ```
 
-Then, you're ready to start:
+#### Launch Gradio for testing
 
 ```bash
-export hf_model_path=./weights/DotsOCR  # Path to your downloaded model weights, Please use a directory name without periods (e.g., `DotsOCR` instead of `dots.ocr`) for the model save path. This is a temporary workaround pending our integration with Transformers.
-export PYTHONPATH=$(dirname "$hf_model_path"):$PYTHONPATH
-sed -i '/^from vllm\.version import __version__ as VLLM_VERSION$/a\
-from DotsOCR import modeling_dots_ocr_vllm' /usr/local/lib/python3.12/dist-packages/vllm-0.10.1.dev0+g6d8d0a24c.d20250825.xpu-py3.12-linux-x86_64.egg/vllm/entrypoints/openai/api_server.py  
-# If you downloaded model weights by yourself, please replace `DotsOCR` by your model saved directory name, and remember to use a directory name without periods (e.g., `DotsOCR` instead of `dots.ocr`) 
-
-# Start the service:
-TORCH_LLM_ALLREDUCE=1 VLLM_USE_V1=1  CCL_ZE_IPC_EXCHANGE=pidfd VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 VLLM_WORKER_MULTIPROC_METHOD=spawn python3 -m vllm.entrypoints.openai.api_server --model YOUR_DOTSOCR_PATH --enforce-eager --host 0.0.0.0 --trust-remote-code --disable-sliding-window --gpu-memory-util=0.8 --no-enable-prefix-caching --max-num-batched-tokens=8192  --disable-log-requests  --max-model-len=40000 --block-size 64 -tp=1 --port 8000 --served-model-name DotsOCR --chat-template-content-format string --dtype bfloat16
+python demo/demo_gradio.py
 ```
 
 ---
@@ -2678,11 +2669,24 @@ python3 -m vllm.entrypoints.openai.api_server \
     --distributed-executor-backend ray
 ```
 
----
+
 At this point, multi-node distributed inference with **PP + TP** is running, coordinated by **Ray** across Node-1 and Node-2.
 
 ---
 
+
+### 2.10 BPE-Qwen Tokenizer
+
+We have integrated the **bpe-qwen tokenizer** to accelerate tokenization for Qwen models.
+
+To enable it when launching the API server, add:
+
+```bash
+--tokenizer-mode bpe_qwen
+```
+
+---
+
 ## 3. Supported Models
 
 | Model Name        | Category         | Notes                          |