GLM-OCR

1. Model Introduction

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization.

The model integrates the 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. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance across diverse document layouts.

Hardware Support: NVIDIA B200/H100/H200

Key Features:

• State-of-the-Art Performance: Achieves 94.62 on OmniDocBench V1.5, ranking #1, and delivers SOTA results across major document understanding benchmarks, including formula recognition, table recognition, and information extraction.
• Optimized for Real-World Scenarios: Specifically optimized for practical business cases, maintaining stable and accurate performance on complex tables, code documents, seals, and other challenging layouts.
• Efficient Inference: With only 0.9B parameters, GLM-OCR supports deployment via vLLM and SGLang, significantly reducing inference latency and compute cost—well suited for high-concurrency and edge deployments.
• Easy to Use: Fully open-sourced with a complete SDK and inference toolchain, enabling one-line invocation and seamless integration into existing systems.

For more details, please refer to the official GLM-OCR model card.

2. SGLang Installation

SGLang offers multiple installation methods. You can choose the most suitable installation method based on your hardware platform and requirements.

Please refer to the official SGLang installation guide for installation instructions.

3. Model Deployment

This section provides deployment configurations optimized for different hardware platforms and use cases.

3.1 Basic Configuration

Interactive Command Generator: Use the configuration selector below to automatically generate the appropriate deployment command for your hardware platform and deployment options. You can optionally enable MTP (Multi-Token Prediction) for faster inference using EAGLE speculative decoding.

import { GLMOCRDeployment } from '/src/snippets/autoregressive/glm-ocr-deployment.jsx'

3.2 Configuration Tips

• CUDA IPC Transport: The SGLANG_USE_CUDA_IPC_TRANSPORT=1 environment variable enables CUDA IPC for transferring multimodal features, which significantly improves TTFT.
• MTP (Multi-Token Prediction): Enable MTP to use EAGLE speculative decoding for faster inference. This feature predicts multiple tokens at once to reduce latency.
• Memory Management: For memory-constrained environments, you may need to adjust --mem-fraction-static and/or --max-running-requests.

4. Model Invocation

4.1 Basic Usage

For basic API usage and request examples, please refer to:

• SGLang Basic Usage Guide
• SGLang OpenAI Vision API Guide

4.2 Advanced Usage

4.2.1 OCR Image Processing

GLM-OCR supports OCR tasks on various document types. Here's a basic example:

import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

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": "Please extract all text from this image."
            }
        ]
    }
]

start = time.time()
response = client.chat.completions.create(
    model="zai-org/GLM-OCR",
    messages=messages,
    max_tokens=2048
)
print(f"Response costs: {time.time() - start:.2f}s")
print(f"Generated text: {response.choices[0].message.content}")

Example Output:

Response costs: 2.29s
Generated text: CINNAMON SUGAR
1 x 17,000 17,000

SUB TOTAL 17,000

GRAND TOTAL 17,000

CASH IDR 20,000

CHANGE DUE 3,000

4.2.2 Complex Document Processing

GLM-OCR excels at processing complex documents including:

• Tables: Accurate extraction of tabular data with structure preservation
• Formulas: Mathematical formula recognition
• Code Documents: Source code extraction from screenshots
• Seals and Stamps: Recognition of seals and stamps in documents
• Multi-layout Documents: Mixed content with text, images, and tables

import time
from openai import OpenAI

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:30000/v1",
    timeout=3600
)

# Example: Processing a document with tables
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image_url",
                "image_url": {
                    "url": "YOUR_DOCUMENT_IMAGE_URL"
                }
            },
            {
                "type": "text",
                "text": "Please extract the table content from this document and format it as markdown."
            }
        ]
    }
]

response = client.chat.completions.create(
    model="zai-org/GLM-OCR",
    messages=messages,
    max_tokens=4096
)
print(response.choices[0].message.content)

5. Benchmark

5.1 Accuracy Benchmark

Document model accuracy on standard benchmarks:

5.1.1 OCRBench Benchmark

• Benchmark Command

python3 -m lmms_eval \
  --model openai_compatible \
  --model_args "model_version=zai-org/GLM-OCR" \
  --tasks ocrbench \
  --batch_size 128 \
  --log_samples \
  --log_samples_suffix "openai_compatible" \
  --output_path ./logs

• Test Result

5.1.2 OmniDocBench V1.5

GLM-OCR achieves 94.62 on OmniDocBench V1.5, ranking #1 among all models, demonstrating state-of-the-art performance across major document understanding benchmarks.