Instructions to use zai-org/GLM-OCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-OCR with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-OCR")
model = AutoModelForImageTextToText.from_pretrained("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?"}
        ]
    },
]
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zai-org/GLM-OCR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-OCR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-OCR",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-OCR

SGLang

How to use zai-org/GLM-OCR with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zai-org/GLM-OCR" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-OCR",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zai-org/GLM-OCR" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-OCR",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-OCR with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-OCR
```

ZHANGYUXUAN-zR Jared Wen commited on Feb 9

Commit

677c6ba

1 Parent(s): 47b6bf4

Update README.md (#25)

Browse files

- Update README.md (a8a31f9345ad2c774da831ad19b592de49920d90)

Co-authored-by: Jared Wen <Jared4Real@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +9 -4

README.md CHANGED Viewed

@@ -22,6 +22,8 @@ library_name: transformers
     👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-OCR/refs/heads/main/resources/wechat.jpg" target="_blank">WeChat</a> and <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community
     <br>
     📍 Use GLM-OCR's <a href="https://docs.z.ai/guides/vlm/glm-ocr" target="_blank">API</a>
 </p>
@@ -59,6 +61,13 @@ For speed, we compared different OCR methods under identical hardware and testin
 ## Usage
 ### vLLM
 1. run
@@ -200,10 +209,6 @@ GLM-OCR currently supports two types of prompt scenarios:
 ⚠️ Note: When using information extraction, the output must strictly adhere to the defined JSON schema to ensure downstream processing compatibility.
-## GLM-OCR SDK
-We provide an easy-to-use SDK for using GLM-OCR more efficiently and conveniently. please check our [github](https://github.com/zai-org/GLM-OCR) to get more detail.
 ## Acknowledgement
 This project is inspired by the excellent work of the following projects and communities:

     👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-OCR/refs/heads/main/resources/wechat.jpg" target="_blank">WeChat</a> and <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community
     <br>
     📍 Use GLM-OCR's <a href="https://docs.z.ai/guides/vlm/glm-ocr" target="_blank">API</a>
+    <br>
+    👉 <a href="https://github.com/zai-org/GLM-OCR" target="_blank">GLM-OCR SDK</a> Recommended
 </p>
 ## Usage
+### Official SDK
+For document parsing tasks, we strongly recommend using our [official SDK](https://github.com/zai-org/GLM-OCR).
+Compared with model-only inference, the SDK integrates PP-DocLayoutV3 and provides a complete, easy-to-use pipeline for document parsing, including layout analysis and structured output generation. This significantly reduces the engineering overhead required to build end-to-end document intelligence systems.
+Note that the SDK is currently designed for document parsing tasks only. For information extraction tasks, please refer to the following section and run inference directly with the model.
 ### vLLM
 1. run
 ⚠️ Note: When using information extraction, the output must strictly adhere to the defined JSON schema to ensure downstream processing compatibility.
 ## Acknowledgement
 This project is inspired by the excellent work of the following projects and communities: