update for new format config of transformers lib / sglang lib (batch 1/1)

README.md

@@ -1,5 +1,14 @@
 ---
 license: mit
+language:
+- en
+- zh
+base_model:
+- zai-org/GLM-4-9B-0414
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- reasoning
 ---
 
 # GLM-4.1V-9B-Thinking
@@ -8,45 +17,56 @@ license: mit
 <img src=https://raw.githubusercontent.com/zai-org/GLM-4.1V-Thinking/99c5eb6563236f0ff43605d91d107544da9863b2/resources/logo.svg width="40%"/>
 </div>
 <p align="center">
-    📖 查看 GLM-4.1V-9B-Thinking <a href="https://arxiv.org/abs/2507.01006" target="_blank">论文</a> 。
+    📖 View the GLM-4.1V-9B-Thinking <a href="https://arxiv.org/abs/2507.01006" target="_blank">paper</a>.
     <br>
-    📍 在 <a href="https://www.bigmodel.cn/dev/api/visual-reasoning-model/GLM-4.1V-Thinking">智谱大模型开放平台</a> 使用 GLM-4.1V-9B-Thinking 的API服务。
+    📍 Using GLM-4.1V-9B-Thinking API at <a href="https://www.bigmodel.cn/dev/api/visual-reasoning-model/GLM-4.1V-Thinking">Zhipu Foundation Model Open Platform</a>
 </p>
 
-## 模型介绍
 
-视觉语言大模型（VLM）已经成为智能系统的关键基石。随着真实世界的智能任务越来越复杂，VLM模型也亟需在基本的多模态感知之外，
-逐渐增强复杂任务中的推理能力，提升自身的准确性、全面性和智能化程度，使得复杂问题解决、长上下文理解、多模态智能体等智能任务成为可能。
+## Model Introduction
 
-基于 [GLM-4-9B-0414](https://github.com/zai-org/GLM-4) 基座模型，我们推出新版VLM开源模型 **GLM-4.1V-9B-Thinking**
-，引入思考范式，通过课程采样强化学习 RLCS（Reinforcement Learning with Curriculum Sampling）全面提升模型能力，
-达到 10B 参数级别的视觉语言模型的最强性能，在18个榜单任务中持平甚至超过8倍参数量的 Qwen-2.5-VL-72B。
-我们同步开源基座模型 **GLM-4.1V-9B-Base**，希望能够帮助更多研究者探索视觉语言模型的能力边界。
+Vision-Language Models (VLMs) have become foundational components of intelligent systems. As real-world AI tasks grow
+increasingly complex, VLMs must evolve beyond basic multimodal perception to enhance their reasoning capabilities in
+complex tasks. This involves improving accuracy, comprehensiveness, and intelligence, enabling applications such as
+complex problem solving, long-context understanding, and multimodal agents.
+
+Based on the [GLM-4-9B-0414](https://github.com/zai-org/GLM-4) foundation model, we present the new open-source VLM model
+**GLM-4.1V-9B-Thinking**, designed to explore the upper limits of reasoning in vision-language models. By introducing
+a "thinking paradigm" and leveraging reinforcement learning, the model significantly enhances its capabilities. It
+achieves state-of-the-art performance among 10B-parameter VLMs, matching or even surpassing the 72B-parameter
+Qwen-2.5-VL-72B on 18 benchmark tasks. We are also open-sourcing the base model GLM-4.1V-9B-Base to
+support further research into the boundaries of VLM capabilities.
 
 ![rl](https://raw.githubusercontent.com/zai-org/GLM-4.1V-Thinking/refs/heads/main/resources/rl.jpeg)
 
-与上一代的 CogVLM2 及 GLM-4V 系列模型相比，**GLM-4.1V-Thinking** 有如下改进：
+Compared to the previous generation models CogVLM2 and the GLM-4V series, **GLM-4.1V-Thinking** offers the
+following improvements:
 
-1. 系列中首个推理模型，不仅仅停留在数学领域，在多个子领域均达到世界前列的水平。
-2. 支持 **64k** 上下长度。
-3. 支持**任意长宽比**和高达 **4k** 的图像分辨率。
-4. 提供支持**中英文双语**的开源模型版本。
+1. The first reasoning-focused model in the series, achieving world-leading performance not only in mathematics but also
+   across various sub-domains.
+2. Supports **64k** context length.
+3. Handles **arbitrary aspect ratios** and up to **4K** image resolution.
+4. Provides an open-source version supporting both **Chinese and English bilingual** usage.
 
-## 榜单信息
+## Benchmark Performance
 
-GLM-4.1V-9B-Thinking 通过引入「思维链」（Chain-of-Thought）推理机制，在回答准确性、内容丰富度与可解释性方面，
-全面超越传统的非推理式视觉模型。在28项评测任务中有23项达到10B级别模型最佳，甚至有18项任务超过8倍参数量的Qwen-2.5-VL-72B。
+By incorporating the Chain-of-Thought reasoning paradigm, GLM-4.1V-9B-Thinking significantly improves answer accuracy,
+richness, and interpretability. It comprehensively surpasses traditional non-reasoning visual models.
+Out of 28 benchmark tasks, it achieved the best performance among 10B-level models on 23 tasks,
+and even outperformed the 72B-parameter Qwen-2.5-VL-72B on 18 tasks.
 
 ![bench](https://raw.githubusercontent.com/zai-org/GLM-4.1V-Thinking/refs/heads/main/resources/bench.jpeg)
 
-## 快速推理
+## Quick Inference
+
+This is a simple example of running single-image inference using the `transformers` library.  
+First, install the `transformers` library from source:
 
-这里展现了一个使用`transformers`进行单张图片推理的代码。首先，从源代码安装`transformers`库。
 ```
 pip install transformers>=4.57.1
 ```
 
-接着按照以下代码运行:
+Then, run the following code:
 
 ```python
 from transformers import AutoProcessor, Glm4vForConditionalGeneration
@@ -59,7 +79,7 @@ messages = [
         "content": [
             {
                 "type": "image",
-                "url": "https://model-demo.oss-cn-hangzhou.aliyuncs.com/Grayscale_8bits_palette_sample_image.png"
+                "url": "https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png"
             },
             {
                 "type": "text",
@@ -68,13 +88,12 @@ messages = [
         ],
     }
 ]
+processor = AutoProcessor.from_pretrained(MODEL_PATH, use_fast=True)
 model = Glm4vForConditionalGeneration.from_pretrained(
     pretrained_model_name_or_path=MODEL_PATH,
     torch_dtype=torch.bfloat16,
     device_map="auto",
 )
-processor = AutoProcessor.from_pretrained(MODEL_PATH, use_fast=True)
-
 inputs = processor.apply_chat_template(
     messages,
     tokenize=True,
@@ -87,6 +106,5 @@ output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:],
 print(output_text)
 ```
 
-
-视频推理，网页端Demo部署等更代码请查看我们的 [github](https://github.com/zai-org/GLM-V)。
-
+For video reasoning, web demo deployment, and more code, please check
+our [GitHub](https://github.com/zai-org/GLM-V).