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
```

Requesting Example for Structured Information Extraction via cURL

#10

by andynoodles - opened Feb 5

Discussion

andynoodles

Feb 5

Description
The model card for GLM-OCR mentions a specific capability for Information Extraction where prompts must follow a strict JSON schema (e.g., for personal ID information). However, the standard examples primarily show simple text recognition.

I am trying to implement a structured extraction using a vLLM or OpenAI-compatible server entry point but need clarification on how to combine the image input with the schema instructions in a single curl request.

Current Implementation Attempt
I am currently using the following curl command, but I want to ensure I am correctly passing the JSON schema prompt to trigger the "Information Extraction" mode rather than standard OCR.

curl -s http://localhost:8000/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
          "model": "zai-org/GLM-OCR",
          "messages": [
               {
                    "role": "user",
                    "content": [
                         {
                              "type": "image_url",
                              "image_url": {
                                   "url": "https://ofasys-multimodal-wlcb-3-toshanghai.oss-accelerate.aliyuncs.com/wpf272043/keepme/image/receipt.png"
                              }
                         },
                         {
                              "type": "text",
                              "text": "请按下列JSON格式输出图中信息:\n{\n    \"id_number\": \"\",\n    \"last_name\": \"\",\n    \"first_name\": \"\",\n    \"date_of_birth\": \"\",\n    \"address\": {\n        \"street\": \"\",\n        \"city\": \"\",\n        \"state\": \"\",\n        \"zip_code\": \"\"\n    },\n    \"dates\": {\n        \"issue_date\": \"\",\n        \"expiration_date\": \"\"\n    },\n    \"sex\": \"\"\n}"
                         }
                    ]
               }
          ],
          "max_tokens": 2048,
          "temperature": 0.0
     }'

Questions
Is the provided curl structure the recommended way to pass the schema?

Should the system prompt or the user prompt contain specific trigger words (e.g., "Information Extraction:") similar to how "Text Recognition:" is used for standard OCR?

Does the model require a specific stop sequence to ensure the JSON is valid and terminates correctly?

iyuge2

Z.ai org Feb 25

@andynoodles Hi, thanks for your attention.

Q1: Is the provided curl structure the recommended way to pass the schema?
A: Yes, the provided curl format is the recommended way to pass the schema.

Q2: Should the system prompt or the user prompt contain specific trigger words (e.g., “Information Extraction:”) similar to how “Text Recognition:” is used for standard OCR?
A: No, this is not required. You can use your normal prompt without adding any special trigger words.

Q3: Does the model require a specific stop sequence to ensure the JSON is valid and terminates correctly?
A: No, a specific stop sequence is not required.

andynoodles

Feb 25

Thanks for reply

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