68 lines
3.5 KiB
Markdown
68 lines
3.5 KiB
Markdown
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<!--Copyright 2025 The ZhipuAI Inc. and The HuggingFace Inc. team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was released on 2025-07-28 and added to Hugging Face Transformers on 2025-08-08.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white"> </div>
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</div>
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# Glm4vMoe
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## Overview
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Vision-language models (VLMs) have become a key cornerstone of intelligent systems. As real-world AI tasks grow increasingly complex, VLMs urgently need to enhance reasoning capabilities beyond basic multimodal perception — improving accuracy, comprehensiveness, and intelligence — to enable complex problem solving, long-context understanding, and multimodal agents.
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Through our open-source work, we aim to explore the technological frontier together with the community while empowering more developers to create exciting and innovative applications.
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[GLM-4.5V](https://huggingface.co/papers/2508.06471) ([Github repo](https://github.com/zai-org/GLM-V)) is based on ZhipuAI’s next-generation flagship text foundation model GLM-4.5-Air (106B parameters, 12B active). It continues the technical approach of [GLM-4.1V-Thinking](https://huggingface.co/papers/2507.01006), achieving SOTA performance among models of the same scale on 42 public vision-language benchmarks. It covers common tasks such as image, video, and document understanding, as well as GUI agent operations.
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Beyond benchmark performance, GLM-4.5V focuses on real-world usability. Through efficient hybrid training, it can handle diverse types of visual content, enabling full-spectrum vision reasoning, including:
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- **Image reasoning** (scene understanding, complex multi-image analysis, spatial recognition)
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- **Video understanding** (long video segmentation and event recognition)
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- **GUI tasks** (screen reading, icon recognition, desktop operation assistance)
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- **Complex chart & long document parsing** (research report analysis, information extraction)
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- **Grounding** (precise visual element localization)
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The model also introduces a **Thinking Mode** switch, allowing users to balance between quick responses and deep reasoning. This switch works the same as in the `GLM-4.5` language model.
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## Glm4vMoeConfig
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[[autodoc]] Glm4vMoeConfig
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## Glm4vMoeTextConfig
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[[autodoc]] Glm4vMoeTextConfig
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## Glm4vMoeTextModel
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[[autodoc]] Glm4vMoeTextModel
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- forward
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## Glm4vMoeModel
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[[autodoc]] Glm4vMoeModel
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- forward
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## Glm4vMoeForConditionalGeneration
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[[autodoc]] Glm4vMoeForConditionalGeneration
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- forward
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