Instructions to use zai-org/GLM-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/GLM-OCR with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="zai-org/GLM-OCR") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-OCR") model = AutoModelForMultimodalLM.from_pretrained("zai-org/GLM-OCR", device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
add technical report
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by JaredforReal - opened
README.md
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📍 Use GLM-OCR's <a href="https://docs.z.ai/guides/vlm/glm-ocr" target="_blank">API</a>
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<br>
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👉 <a href="https://github.com/zai-org/GLM-OCR" target="_blank">GLM-OCR SDK</a> Recommended
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</p>
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The GLM-OCR model is released under the MIT License.
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The complete OCR pipeline integrates [PP-DocLayoutV3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3) for document layout analysis, which is licensed under the Apache License 2.0. Users should comply with both licenses when using this project.
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📍 Use GLM-OCR's <a href="https://docs.z.ai/guides/vlm/glm-ocr" target="_blank">API</a>
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<br>
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👉 <a href="https://github.com/zai-org/GLM-OCR" target="_blank">GLM-OCR SDK</a> Recommended
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<br>
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📖 <a href="https://arxiv.org/abs/2603.10910" target="_blank"> Technical Report</a>
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</p>
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The GLM-OCR model is released under the MIT License.
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The complete OCR pipeline integrates [PP-DocLayoutV3](https://huggingface.co/PaddlePaddle/PP-DocLayoutV3) for document layout analysis, which is licensed under the Apache License 2.0. Users should comply with both licenses when using this project.
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## Citation
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If you find GLM-OCR useful in your research, please cite our technical report:
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```bibtex
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@misc{duan2026glmocrtechnicalreport,
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title={GLM-OCR Technical Report},
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author={Shuaiqi Duan and Yadong Xue and Weihan Wang and Zhe Su and Huan Liu and Sheng Yang and Guobing Gan and Guo Wang and Zihan Wang and Shengdong Yan and Dexin Jin and Yuxuan Zhang and Guohong Wen and Yanfeng Wang and Yutao Zhang and Xiaohan Zhang and Wenyi Hong and Yukuo Cen and Da Yin and Bin Chen and Wenmeng Yu and Xiaotao Gu and Jie Tang},
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year={2026},
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eprint={2603.10910},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2603.10910},
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}
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```
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