huggingface--transformers/docs/source/en/model_doc/glm_ocr.md

This model was contributed to Hugging Face Transformers on 2026-01-27.

GLM-OCR

Overview

GLM-OCR is a multimodal OCR (Optical Character Recognition) model designed for complex document understanding from Z.ai. The model combines a CogViT visual encoder (pre-trained on large-scale image-text data), a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder.

Key features of GLM-OCR include:

• Lightweight: Only 0.9B parameters while achieving state-of-the-art performance (94.62 on OmniDocBench V1.5)
• Multi-task: Excels at text recognition, formula recognition, table recognition, and information extraction
• Multi-modal: Processes document images for text, formula, and table extraction

This model was contributed by the zai-org team. The original code can be found here.

Usage example

Single image inference

from transformers import AutoProcessor, GlmOcrForConditionalGeneration


model_id = "zai-org/GLM-OCR"

processor = AutoProcessor.from_pretrained(model_id)
model = GlmOcrForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"},
            {"type": "text", "text": "Text Recognition:"},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

output = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(output[0], skip_special_tokens=True))

Batch inference

The model supports batching multiple images for efficient processing.

from transformers import AutoProcessor, GlmOcrForConditionalGeneration


model_id = "zai-org/GLM-OCR"

processor = AutoProcessor.from_pretrained(model_id)
model = GlmOcrForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
)

# First document
message1 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg"},
            {"type": "text", "text": "Text Recognition:"},
        ],
    }
]

# Second document
message2 = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
            {"type": "text", "text": "Text Recognition:"},
        ],
    }
]

messages = [message1, message2]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
    padding=True,
).to(model.device)

output = model.generate(**inputs, max_new_tokens=128)
print(processor.batch_decode(output, skip_special_tokens=True))

Flash Attention 2

GLM-OCR supports Flash Attention 2 for faster inference. First, install the latest version of Flash Attention:

pip install -U flash-attn --no-build-isolation

Then load the model with one of the supported kernels of the kernels-community:

from transformers import GlmOcrForConditionalGeneration


model = GlmOcrForConditionalGeneration.from_pretrained(
    "zai-org/GLM-OCR",
    attn_implementation="kernels-community/flash-attn2",  # other options: kernels-community/vllm-flash-attn3, kernels-community/paged-attention
    device_map="auto",
)

GlmOcrConfig

autodoc GlmOcrConfig

GlmOcrVisionConfig

autodoc GlmOcrVisionConfig

GlmOcrTextConfig

autodoc GlmOcrTextConfig

GlmOcrVisionModel

autodoc GlmOcrVisionModel

• forward

GlmOcrTextModel

autodoc GlmOcrTextModel

• forward

GlmOcrModel

autodoc GlmOcrModel

• forward

GlmOcrForConditionalGeneration

autodoc GlmOcrForConditionalGeneration

• forward