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

# GLM-OCR

<div class="flex flex-wrap space-x-1">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
</div>

## Overview

[GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) is a multimodal OCR (Optical Character Recognition) model designed for complex document understanding from [Z.ai](https://github.com/zai-org/GLM-OCR). 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](https://huggingface.co/zai-org) team.
The original code can be found [here](https://github.com/zai-org/GLM-OCR).

## Usage example

### Single image inference

```python

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.

```python

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:

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

Then load the model with one of the supported kernels of the [kernels-community](https://huggingface.co/kernels-community):

```python

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