polish

Signed-off-by: youkaichao <[email protected]>

Author: youkaichao

_posts/2025-08-15-glm45-vllm.md

@@ -1,15 +1,15 @@
 ---
 layout: post
-title: "Use vLLM to deploy GLM-4.5 and GLM-4.5V model"
+title: "GLM-4.5 Meets vLLM: Built for Intelligent Agents"
 author: "Yuxuan Zhang"
 image: /assets/logos/vllm-logo-text-light.png
 ---
 
-# Use vLLM to deploy GLM-4.5 and GLM-4.5V model
+## Introduction
 
-## Model Introduction
+[General Language Model (GLM)](https://aclanthology.org/2022.acl-long.26/) is a family of foundation models created by Zhipu.ai (now renamed to [Z.ai](https://z.ai/)). The GLM team has long-term collaboration with vLLM team, dating back to the early days of vLLM and the popular [ChatGLM model series](https://github.com/zai-org/ChatGLM-6B). Recently, the GLM team released the GLM-4.5 and GLM-4.5V model series, which are designed for intelligent agents. They are the top trending models in Huggingface model hub right now.
 
-The GLM-4.5 series models are foundation models designed for intelligent agents. GLM-4.5 has 355 billion total
+GLM-4.5 has 355 billion total
 parameters with 32 billion active parameters, while GLM-4.5-Air adopts a more compact design with 106 billion total
 parameters and 12 billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities
 to meet the complex demands of intelligent agent applications.
@@ -28,10 +28,10 @@ among models of the same scale on 42 public vision-language benchmarks.
 
 ![bench_45v](https://raw.githubusercontent.com/zai-org/GLM-V/refs/heads/main/resources/bench_45v.jpeg)
 
-To get more information about GLM-4.5 and GLM-4.5V, please refer to the [GLM-4.5](https://github.com/zai-org/GLM-4.5)
+To get more information about GLM-4.5 and GLM-4.5V, please refer to [GLM-4.5](https://github.com/zai-org/GLM-4.5)
 and [GLM-V](https://github.com/zai-org/GLM-V).
 
-this blog will guide users on how to use vLLM to accelerate inference for the GLM-4.5V and GLM-4.5 model series on
+This blog will guide users on how to use vLLM to accelerate inference for the GLM-4.5V and GLM-4.5 model series on
 NVIDIA Blackwell and Hopper GPUs.
 
 ## Installation
@@ -78,19 +78,19 @@ vllm serve zai-org/GLM-4.5V \
   `extra_body={"chat_template_kwargs": {"enable_thinking": False}}`
 + If you're using 8x H100 GPUs and encounter insufficient memory when running the GLM-4.5 model, you'll need
   `--cpu-offload-gb 16`.
-+ If you encounter `flash infer` issues, use `VLLM_ATTENTION_BACKEND=XFORMERS` as a temporary replacement. You can also
-  specify `TORCH_CUDA_ARCH_LIST='9.0+PTX'` to use `flash infer`, different GPUs have different TORCH_CUDA_ARCH_LIST
++ If you encounter `flash_infer` issues, use `VLLM_ATTENTION_BACKEND=XFORMERS` as a temporary replacement. You can also
+  specify `TORCH_CUDA_ARCH_LIST='9.0+PTX'` to use `flash_infer`, different GPUs have different TORCH_CUDA_ARCH_LIST
   values, please check accordingly.
-+ vllm v0 is not support our model.
++ vLLM v0 is not support our model.
 
 ### Grounding in GLM-4.5V
 
 GLM-4.5V equips precise grounding capabilities. Given a prompt that requests the location of a specific object, GLM-4.5V
 is able to reasoning step-by-step and identify the bounding boxes of the target object. The query prompt supports
-complex descriptions of the target object as well as specified output formats, for example:
+complex descriptions of the target object as well as specified output formats. Example prompts are:
 
-> - Help me to locate <expr> in the image and give me its bounding boxes.
-> - Please pinpoint the bounding box [[x1,y1,x2,y2], …] in the image as per the given description. <expr>
+- Help me to locate `<expr>` in the image and give me its bounding boxes.
+- Please pinpoint the bounding box `[[x1,y1,x2,y2], …]` in the image as per the given description. <expr>
 
 Here, `<expr>` is the description of the target object. The output bounding box is a quadruple $$[x_1,y_1,x_2,y_2]$$
 composed of the coordinates of the top-left and bottom-right corners, where each value is normalized by the image
@@ -100,16 +100,12 @@ In the response, the special tokens `<|begin_of_box|>` and `<|end_of_box|>` are
 the answer. The bracket style may vary ([], [[]], (), <>, etc.), but the meaning is the same: to enclose the coordinates
 of the box.
 
-## Cooperation with vLLM and Z.ai Team
+## Cooperation with vLLM and GLM Team
 
-During the release of the GLM-4.5 and GLM-4.5V models, the vLLM team worked closely with the Z.ai team, providing
-extensive support in addressing issues related to the model launch.  
-The GLM-4.5 and GLM-4.5V models provided by the Z.ai team were modified in the vLLM implementation PR, including (but
-not limited to) resolving [CUDA Core Dump](./2025-08-11-cuda-debugging.md) debugging issues and FP8 model accuracy
-alignment problems.  
-They also ensured that the vLLM `main` branch had full support for the open-source GLM-4.5 series before the models were
-released.
+Before the release of the GLM-4.5 and GLM-4.5V models, the vLLM team worked closely with the GLM team, providing
+extensive support in addressing issues related to the model launch, ensuring that the vLLM `main` branch had full
+support for the open-source GLM-4.5 series before the models were released.
 
 ## Acknowledgement
 
-We would like to thank the vLLM team members who contributed to this effort are: Simon Mo, Kaichao You.
+We would like to thank the vLLM team members who contributed to this effort, including: Simon Mo, Kaichao You.