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
```

GLM-OCR: A Tiny 0.9B-Parameter Model That Punches Far Above Its Weight

by Javedalam - opened Feb 3

Discussion

Javedalam

Feb 3

Released today by Z.ai, GLM-OCR is a compact vision-language model designed specifically for document understanding. At just 0.9 billion parameters, it belongs to a new generation of lightweight AI systems proving that raw model size is no longer the only path to high performance.

Despite its small footprint, GLM-OCR posts exceptionally strong results across major document benchmarks. It scores 94.6 on OmniDocBench, 94.0 on OCRBench, and an impressive 96.5 on UniMERNet for formula recognition—numbers that place it alongside, and in some cases ahead of, significantly larger specialized OCR models. The takeaway is clear: efficiency is rapidly becoming a defining feature of modern AI design.

Developed by Z.ai, a research group focused on advancing multimodal foundation models, GLM-OCR reflects a broader shift toward highly optimized architectures that deliver serious capability without requiring massive compute resources.

In practical testing, the model ran successfully in Google Colab on an NVIDIA L4 GPU, demonstrating that advanced document AI is no longer restricted to large research clusters. Engineers, researchers, and developers can now deploy high-quality OCR workflows from relatively accessible hardware.

GLM-OCR signals an important trend in artificial intelligence: smaller, purpose-built models are beginning to rival heavyweight systems while being dramatically easier to run. For anyone working with scanned documents, PDFs, or structured text extraction, this release is a strong indicator of where efficient multimodal AI is heading next.

The model is running in this google colab notebook

https://colab.research.google.com/drive/1SiXjxPdb-7UJWhtAjPrMZYqPJhyLk9Rc?usp=sharing

fflyesst

Feb 3

thoughtful for real

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