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README.md

-# GLM-4.2V
+# GLM-4.1V
+## 论文
+`GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning`
+- https://arxiv.org/abs/2507.01006
 
+## 模型结构
+GLM-4.1V-Thinking 有三部分组成：
+1. 一个视觉 Transformer 编码器，用于处理和编码图像及视频；
+2. 一个多层感知机投影器，用于将视觉特征与标记对齐；
+3. 一个大型语言模型作为语言解码器，用于处理多模态标记并生成标记补全内容。
+
+<div align=center>
+    <img src="./doc/model.png"/>
+</div>
+
+## 算法原理
+GLM-4.1V-thinging旨在探索视觉语言模型推理能力的上限，通过引入“思考范式”并利用采样强化学习 RLCS（Reinforcement Learning with Curriculum Sampling）全面提升模型能力。在 100 亿参数的视觉语言模型中，其性能处于领先地位，在 18 项基准测试任务中与 720 亿参数的 Qwen-2.5-VL-72B 相当甚至更优。
+
+GLM-4.1V-Thinking能够将图像和视频以其原始的分辨率和宽高比进行识别。对于视频输入，会在每帧后面插入额外的时间索引标记，以增强模型的时间理解能力。
+
+与上一代的 CogVLM2 及 GLM-4V 系列模型相比，**GLM-4.1V-Thinking** 有如下改进：
+
+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。
+
+<div align=center>
+    <img src="./doc/methods.png"/>
+</div>
+
+## 环境配置
+### 硬件需求
+DCU型号：K100_AI，节点数量：1台，卡数：1张。
+`-v 路径`、`docker_name`和`imageID`根据实际情况修改
+
+### Docker（方法一）
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250711
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/glm-4.1v_pytorch
+pip install transformers==4.53.2
+```
+### Dockerfile（方法二）
+此处提供dockerfile的使用方法
+```
+docker build --no-cache -t glm-4.1v:latest .
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/glm-4.1v_pytorch
+pip install transformers==4.53.2
+```
+### Anaconda（方法三）
+此处提供本地配置、编译的详细步骤，例如：
+
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
+```
+DTK驱动：dtk25.04.1
+python：python3.10
+torch: 2.4.1+das.opt1.dtk25041
+```
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
+
+其它非深度学习库参照requirements.txt安装：
+```
+pip install transformers==4.53.2
+```
+
+## 数据集
+[LLaMA-Factory](https://developer.sourcefind.cn/codes/OpenDAS/llama-factory)已经支持本模型的微调。以下是构建数据集的说明，这是一个使用了两张图片的数据集。你需要将数据集整理为`finetune.json`，然后根据llama-factory中的数据配置进行相关修改。
+
+```json
+[
+  {
+    "messages": [
+      {
+        "content": "<image>Who are they?",
+        "role": "user"
+      },
+      {
+        "content": "<think>\nUser ask me to observe the image and get the answer. I Know they are Kane and Gretzka from Bayern Munich.</think>\n<answer>They're Kane and Gretzka from Bayern Munich.</answer>",
+        "role": "assistant"
+      },
+      {
+        "content": "<image>What are they doing?",
+        "role": "user"
+      },
+      {
+        "content": "<think>\nI need to observe what this people are doing. Oh, They are celebrating on the soccer field.</think>\n<answer>They are celebrating on the soccer field.</answer>",
+        "role": "assistant"
+      }
+    ],
+    "images": [
+      "mllm_demo_data/1.jpg",
+      "mllm_demo_data/2.jpg"
+    ]
+  }
+]
+```
+
+1. `<think> XXX </think>` 中的部分不会被存放为历史记录和微调。
+2. `<image>` 标签会被替换成图片信息。
+
+## 训练
+### Llama Factory 微调方法(推荐)
+因为transformers版本与[LLaMA-Factory](https://developer.sourcefind.cn/codes/OpenDAS/llama-factory)版本不一致， 启动训练前需要先增加环境变量来跳过版本检查，环境变量如下：
+
+```
+export DISABLE_VERSION_CHECK=1
+```
+#### 全参微调
+SFT训练脚本示例，参考`llama-factory/train_full`下对应yaml文件。
+
+**参数修改**：
+- **--model_name_or_path**: 修改为待训练模型地址，如 `/data/GLM-4.1V-9B-Thinking`
+- **--dataset**: 微调训练集名称，可选数据集请参考 `llama-factory/data/dataset_info.json`
+- **--template**: 将 default 修改为 `glm4v`
+- **--output_dir**: 模型保存地址
+
+其他参数如：`--learning_rate`、`--save_steps`可根据自身硬件及需求进行修改。
+
+#### lora微调
+SFT训练脚本示例，参考`llama-factory/train_lora`下对应yaml文件。
+参数解释同[#全参微调](#全参微调)
+
+## 推理
+- `trans_infer_transformers.py`: 使用`transformers`库进行单次对话推理。
+- `trans_infer_cli.py`: 使用`transformers`库作为推理后端的命令行交互脚本。你可以使用它进行连续对话。
+
+```
+# 单次推理启动命令如下
+python inference/trans_infer_transformers.py
+# 交互对话启动命令如下：
+python inference/trans_infer_cli.py
+```
+
+## result
+
+<div align=center>
+    <img src="./doc/results-dcu.png"/>
+</div>
+
+### 精度
+测试数据：[test data](https://developer.sourcefind.cn/codes/OpenDAS/llama-factory/-/blob/master/data/alpaca_en_demo.json)，使用的加速卡:K100_AI。
+模型：GLM-4.1V-9B-Thinking
+
+| device | iters | train_loss |
+| :------: | :------: | :------: |
+| A800 | 375 |  |
+| K100_AI | 375 | 0.5264 |
+
+## 应用场景
+### 算法类别
+`对话问答`
+
+### 热点应用行业
+`制造,广媒,家居,教育`
+
+## 预训练权重
+- [GLM-4.1V-9B-Base](https://huggingface.co/THUDM/GLM-4.1V-9B-Base)
+- [GLM-4.1V-9B-Thinking](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/glm-4.1v_pytorch
+
+## 参考资料
+- https://github.com/THUDM/GLM-4.1V-Thinking

README_origin.md

+# GLM-4.1V-Thinking
+
+[中文阅读](./README_zh.md)
+
+<div align="center">
+<img src=resources/logo.svg width="40%"/>
+</div>
+<p align="center">
+    👋 Join our <a href="resources/WECHAT.md" target="_blank">Wechat</a> or <a href="https://discord.com/invite/8cnQKdAprg" target="_blank">Discord</a>
+    <br>
+    📖 View the GLM-4.1V-9B-Thinking <a href="https://arxiv.org/abs/2507.01006" target="_blank">paper</a>.
+    <br>
+    💡 Try the <a href="https://huggingface.co/spaces/THUDM/GLM-4.1V-9B-Thinking-API-Demo" target="_blank">Hugging Face</a> or <a href="https://modelscope.cn/studios/ZhipuAI/GLM-4.1V-9B-Thinking-Demo" target="_blank">ModelScope</a> online demo for GLM-4.1V-9B-Thinking.
+    <br>
+    📍 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>
+
+## Project Updates
+
+- 🔥 **News**: `2025/07/16`: We have open-sourced the **VLM Reward System** used in training GLM-4.1V-Thinking! Check out the [repo here](https://github.com/THUDM/GLM-4.1V-Thinking/tree/main/glmv_reward) and try it locally: `python examples/reward_system_demo.py`
+
+- **News**: `2025/07/02`: The [GLM-4.1V-9B-Thinking series](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking) is now open-sourced! Supports enhanced visual reasoning and agent interaction.
+
+- **News**: `2025/07/01`: We released the [Technical Report](https://arxiv.org/abs/2507.01006) for GLM-4.1V-Thinking.
+
+## Model Introduction
+
+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/THUDM/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](resources/rl.jpeg)
+
+Compared to the previous generation models CogVLM2 and the GLM-4V series, **GLM-4.1V-Thinking** offers the
+following improvements:
+
+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.
+
+## Model Information
+
+### Model Download Links
+
+| Model                | Download Links                                                                                                                                      | Model Type      |
+|----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|-----------------|
+| GLM-4.1V-9B-Thinking | [🤗 Hugging Face](https://huggingface.co/THUDM/GLM-4.1V-9B-Thinking)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.1V-9B-Thinking) | Reasoning Model |
+| GLM-4.1V-9B-Base     | [🤗 Hugging Face](https://huggingface.co/THUDM/GLM-4.1V-9B-Base)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.1V-9B-Base)         | Base Model      |
+
+The model's algorithm implementation can be found in the
+official [transformers](https://github.com/huggingface/transformers/tree/main/src/transformers/models/glm4v) repository.
+
+### Runtime Requirements
+
+#### Inference
+
+| Device (Single GPU) | Framework    | Min Memory | Speed              | Precision |
+|---------------------|--------------|------------|--------------------|-----------|
+| NVIDIA A100         | transformers | 22GB       | 14 - 22 Tokens / s | BF16      |
+| NVIDIA A100         | vLLM         | 22GB       | 60 - 70 Tokens / s | BF16      |
+
+#### Fine-tuning
+
+The following results are based on image fine-tuning using the [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+toolkit.
+
+| Device (Cluster) | Strategy   | Min Memory / # of GPUs | Batch Size (per GPU) | Freezing    |
+|------------------|------------|------------------------|----------------------|-------------|
+| NVIDIA A100      | LORA       | 21GB / 1 GPU           | 1                    | Freeze VIT  |
+| NVIDIA A100      | FULL ZERO2 | 280GB / 4 GPUs         | 1                    | Freeze VIT  |
+| NVIDIA A100      | FULL ZERO3 | 192GB / 4 GPUs         | 1                    | Freeze VIT  |
+| NVIDIA A100      | FULL ZERO2 | 304GB / 4 GPUs         | 1                    | No Freezing |
+| NVIDIA A100      | FULL ZERO3 | 210GB / 4 GPUs         | 1                    | No Freezing |
+
+> Note: Fine-tuning with Zero2 may result in zero loss; Zero3 is recommended for stable training.
+
+## Benchmark Performance
+
+Based on the [GLM-4-9B-0414](https://github.com/THUDM/GLM-4) foundation model, we present the new open-source VLM model
+**GLM-4.1V-9B-Thinking**, which introduces a "thinking" paradigm and leverages Reinforcement Learning with Curriculum
+Sampling (RLCS) to comprehensively enhance model capabilities.
+It achieves state-of-the-art performance among vision-language models at the 10B parameter scale, matching or even
+surpassing the 72B Qwen-2.5-VL on 18 benchmark tasks.
+We also open-source the base model **GLM-4.1V-9B-Base** to support further research on the frontier of vision-language
+models.
+
+![bench](resources/bench.jpeg)
+
+## Model Inference
+
+All inference scripts are located in the `inference` folder and include:
+
+- `trans_infer_cli.py`: A command-line interactive script using the `transformers` library as the backend. It supports
+  multi-turn dialogue.
+- `trans_infer_gradio.py`: A Gradio-based web UI script using the `transformers` backend. It supports multimodal inputs
+  such as images, videos, PDFs, and PPTs.
+- OpenAI-compatible API service with `vllm`, along with a simple request example provided in `vllm_api_request.py`.
+
+    ```shell
+    vllm serve THUDM/GLM-4.1V-9B-Thinking --limit-mm-per-prompt '{"image":32}' --allowed-local-media-path /
+    ```
+
+  - If `--limit-mm-per-prompt` is not specified, only 1 image is supported. The model supports a maximum of 1 video or
+    300 images per input — it does **not** support simultaneous image and video inputs.
+  - `--allowed-local-media-path` must be set to permit access to local multimodal inputs.
+
+- `trans_infer_bench`: Academic benchmarking script for inference with `GLM-4.1V-9B-Thinking`. Key features:
+  - Automatically interrupts thinking if it exceeds 8192 tokens and appends `</think><answer>` to prompt the model to
+    generate a final answer.
+  - Demonstrates video-based input; for other modalities, modifications are required.
+  - Only a `transformers` version is provided. For `vLLM`, a custom implementation is needed to support this logic.
+
+- `vllm_request_gui_agent.py`: This script demonstrates how to handle model responses and construct prompts for GUI
+  Agent use cases. It covers strategies for mobile, desktop, and web environments, and can be integrated into your
+  application framework. For detailed documentation about GUI Agent, please refer to [this file](resources/agent.md).
+
+- For Ascend NPU Inference, Check [here](https://gitee.com/ascend/MindSpeed-MM/tree/master/examples/glm4.1v/README.md).
+
+## Model Fine-tuning
+
+[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) now supports fine-tuning of this model. Below is an example
+dataset using two images. Prepare your dataset in a `finetune.json` file like the following:
+
+```json
+[
+  {
+    "messages": [
+      {
+        "content": "<image>Who are they?",
+        "role": "user"
+      },
+      {
+        "content": "<think>\nUser ask me to observe the image and get the answer. I Know they are Kane and Gretzka from Bayern Munich.</think>\n<answer>They're Kane and Gretzka from Bayern Munich.</answer>",
+        "role": "assistant"
+      },
+      {
+        "content": "<image>What are they doing?",
+        "role": "user"
+      },
+      {
+        "content": "<think>\nI need to observe what this people are doing. Oh, They are celebrating on the soccer field.</think>\n<answer>They are celebrating on the soccer field.</answer>",
+        "role": "assistant"
+      }
+    ],
+    "images": [
+      "mllm_demo_data/1.jpg",
+      "mllm_demo_data/2.jpg"
+    ]
+  }
+]
+```
+
+1. Content inside `<think> ... </think>` will **not** be stored in the conversation history or used during fine-tuning.
+2. The `<image>` tag will be replaced with actual image data during preprocessing.
+
+After preparing the dataset, you can proceed with fine-tuning using the standard LLaMA-Factory pipeline.
+
+## Model License
+
+- The code in this repository is released under the [Apache License 2.0](LICENSE).
+- The models **GLM-4.1V-9B-Thinking** and **GLM-4.1V-9B-Base** are both licensed under the **MIT License**.
+
+## Citation
+
+If you find our work helpful, please consider citing the following paper.
+
+```bibtex
+@misc{glmvteam2025glm41vthinkingversatilemultimodalreasoning,
+      title={GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning}, 
+      author={GLM-V Team and Wenyi Hong and Wenmeng Yu and Xiaotao Gu and Guo Wang and Guobing Gan and Haomiao Tang and Jiale Cheng and Ji Qi and Junhui Ji and Lihang Pan and Shuaiqi Duan and Weihan Wang and Yan Wang and Yean Cheng and Zehai He and Zhe Su and Zhen Yang and Ziyang Pan and Aohan Zeng and Baoxu Wang and Boyan Shi and Changyu Pang and Chenhui Zhang and Da Yin and Fan Yang and Guoqing Chen and Jiazheng Xu and Jiali Chen and Jing Chen and Jinhao Chen and Jinghao Lin and Jinjiang Wang and Junjie Chen and Leqi Lei and Letian Gong and Leyi Pan and Mingzhi Zhang and Qinkai Zheng and Sheng Yang and Shi Zhong and Shiyu Huang and Shuyuan Zhao and Siyan Xue and Shangqin Tu and Shengbiao Meng and Tianshu Zhang and Tianwei Luo and Tianxiang Hao and Wenkai Li and Wei Jia and Xin Lyu and Xuancheng Huang and Yanling Wang and Yadong Xue and Yanfeng Wang and Yifan An and Yifan Du and Yiming Shi and Yiheng Huang and Yilin Niu and Yuan Wang and Yuanchang Yue and Yuchen Li and Yutao Zhang and Yuxuan Zhang and Zhanxiao Du and Zhenyu Hou and Zhao Xue and Zhengxiao Du and Zihan Wang and Peng Zhang and Debing Liu and Bin Xu and Juanzi Li and Minlie Huang and Yuxiao Dong and Jie Tang},
+      year={2025},
+      eprint={2507.01006},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2507.01006}, 
+}
+```

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/vllm:0.8.5-ubuntu22.04-dtk25.04.1-rc5-das1.6-py3.10-20250711
\ No newline at end of file

inference/trans_infer_cli.py

+"""
+A command-line interface for chatting with GLM-4.1V-9B-Thinkng model supporting images and videos.
+
+Examples:
+    # Text-only chat
+    python trans_infer_cli.py
+    # Chat with single image
+    python trans_infer_cli.py --image_paths /path/to/image.jpg
+    # Chat with multiple images
+    python trans_infer_cli.py --image_paths /path/to/img1.jpg /path/to/img2.png /path/to/img3.png
+    # Chat with single video
+    python trans_infer_cli.py --video_path /path/to/video.mp4
+    # Custom generation parameters
+    python trans_infer_cli.py --temperature 0.8 --top_k 5 --max_tokens 4096
+
+Notes:
+    - Media files are loaded once at startup and persist throughout the conversation
+    - Type 'exit' to quit the chat
+    - Chat with images and video is NOT allowed
+    - The model will remember the conversation history and can reference uploaded media in subsequent turns
+"""
+
+import argparse
+import re
+
+import torch
+from transformers import AutoProcessor, Glm4vForConditionalGeneration
+
+
+def build_content(image_paths, video_path, text):
+    content = []
+    if image_paths:
+        for img_path in image_paths:
+            content.append({"type": "image", "url": img_path})
+    if video_path:
+        content.append({"type": "video", "url": video_path})
+    content.append({"type": "text", "text": text})
+    return content
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_path", type=str, default="THUDM/GLM-4.1V-9B-Thinking")
+    parser.add_argument("--image_paths", type=str, nargs="*", default=None)
+    parser.add_argument("--video_path", type=str, default=None)
+    parser.add_argument("--max_tokens", type=int, default=8192)
+    parser.add_argument("--temperature", type=float, default=1.0)
+    parser.add_argument("--repetition_penalty", type=float, default=1.0)
+    parser.add_argument("--top_k", type=int, default=2)
+
+    args = parser.parse_args()
+    processor = AutoProcessor.from_pretrained(args.model_path, use_fast=True)
+    model = Glm4vForConditionalGeneration.from_pretrained(
+        args.model_path, torch_dtype=torch.bfloat16, device_map="cuda:0"
+    )
+    messages = []
+    first_turn = True
+    if args.image_paths is not None and args.video_path is not None:
+        raise ValueError(
+            "Chat with images and video is NOT allowed. Please use either --image_paths or --video_path, not both."
+        )
+
+    while True:
+        question = input("\nUser: ").strip()
+        if question.lower() == "exit":
+            break
+        if first_turn:
+            content = build_content(args.image_paths, args.video_path, question)
+            first_turn = False
+        else:
+            content = [{"type": "text", "text": question}]
+        messages.append({"role": "user", "content": content})
+        inputs = processor.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+        ).to(model.device)
+        output = model.generate(
+            **inputs,
+            max_new_tokens=args.max_tokens,
+            repetition_penalty=args.repetition_penalty,
+            do_sample=args.temperature > 0,
+            top_k=args.top_k,
+            temperature=args.temperature if args.temperature > 0 else None,
+        )
+        raw = processor.decode(
+            output[0][inputs["input_ids"].shape[1] : -1], skip_special_tokens=False
+        )
+        match = re.search(r"<answer>(.*?)</answer>", raw, re.DOTALL)
+        answer = match.group(1).strip() if match else ""
+        messages.append(
+            {"role": "assistant", "content": [{"type": "text", "text": answer}]}
+        )
+        print(f"Assistant: {raw}")
+
+
+if __name__ == "__main__":
+    main()

inference/trans_infer_transformers.py

+from transformers import AutoProcessor, Glm4vForConditionalGeneration
+import torch
+import argparse
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_path", type=str, default="THUDM/GLM-4.1V-9B-Thinking", help="Path to the model")
+args = parser.parse_args()
+
+
+if __name__ == "__main__":
+    # Example usage
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image",
+                    "url": "doc/Grayscale_8bits_palette_sample_image.png"
+                },
+                {
+                    "type": "text",
+                    "text": "describe this image"
+                }
+            ],
+        }
+    ]
+    # Load model and processor
+    processor = AutoProcessor.from_pretrained(args.model_path, use_fast=True)
+    model = Glm4vForConditionalGeneration.from_pretrained(
+        pretrained_model_name_or_path=args.model_path,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+    )
+    # process inputs
+    inputs = processor.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to(model.device)
+    # generate
+    generated_ids = model.generate(**inputs, max_new_tokens=8192)
+
+    output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
+    print("output:\n", output_text)

llama-factory/train_full/glm_4.1v_full_sft.yaml

+### model
+model_name_or_path: THUDM/GLM-4.1V-9B-Thinking
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: full
+deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
+
+### dataset
+dataset: alpaca_en_demo
+template: glm4v
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/glm-4.1v-9b-thinking/full/sft
+logging_steps: 1
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-5
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500

llama-factory/train_lora/glm_4.1v_lora_sft_ds3.yaml

+### model
+model_name_or_path: THUDM/GLM-4.1V-9B-Thinking
+trust_remote_code: true
+
+### method
+stage: sft
+do_train: true
+finetuning_type: lora
+lora_rank: 8
+lora_target: all
+deepspeed: examples/deepspeed/ds_z3_config.json  # choices: [ds_z0_config.json, ds_z2_config.json, ds_z3_config.json]
+
+### dataset
+dataset: alpaca_en_demo
+template: glm4v
+cutoff_len: 2048
+max_samples: 1000
+overwrite_cache: true
+preprocessing_num_workers: 16
+dataloader_num_workers: 4
+
+### output
+output_dir: saves/glm-4.1v-9b-thinking/lora/sft
+logging_steps: 10
+save_steps: 500
+plot_loss: true
+overwrite_output_dir: true
+save_only_model: false
+report_to: none  # choices: [none, wandb, tensorboard, swanlab, mlflow]
+
+### train
+per_device_train_batch_size: 1
+gradient_accumulation_steps: 2
+learning_rate: 1.0e-4
+num_train_epochs: 3.0
+lr_scheduler_type: cosine
+warmup_ratio: 0.1
+bf16: true
+ddp_timeout: 180000000
+resume_from_checkpoint: null
+
+### eval
+# eval_dataset: alpaca_en_demo
+# val_size: 0.1
+# per_device_eval_batch_size: 1
+# eval_strategy: steps
+# eval_steps: 500

model.properties

+# 模型唯一标识
+modelCode=1668
+# 模型名称
+modelName=GLM-4.1V_pytorch
+# 模型描述
+modelDescription=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。
+# 应用场景
+appScenario=推理,训练,对话问答,制造,广媒,家居,教育
+# 框架类型
+frameType=pytorch