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README.md

@@ -31,17 +31,13 @@ GLM-OCR is a multimodal OCR model for complex document understanding, built on t
 
 **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.
+- **State-of-the-Art Performance**: Achieves a score of 94.62 on OmniDocBench V1.5, ranking #1 overall, and delivers state-of-the-art results across major document understanding benchmarks, including formula recognition, table recognition, and information extraction.
 
-- Optimized for Real-World Scenarios
-Specifically optimized for practical business use cases, maintaining stable and accurate performance on complex tables, code documents, seals, and other challenging layouts.
+- **Optimized for Real-World Scenarios**: Designed and optimized for practical business use cases, maintaining robust performance on complex tables, code-heavy documents, seals, and other challenging real-world 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.
+- **Efficient Inference**: With only 0.9B parameters, GLM-OCR supports deployment via vLLM, SGLang, and Ollama, significantly reducing inference latency and compute cost, making it ideal for high-concurrency services and edge deployments.
 
-- Easy to Use
-Fully open-sourced with a complete [SDK](https://github.com/zai-org/GLM-OCR) and inference toolchain, enabling one-line invocation and seamless integration into existing systems.
+- **Easy to Use**: Fully open-sourced and equipped with a comprehensive [SDK](https://github.com/zai-org/GLM-OCR) and inference toolchain, offering simple installation, one-line invocation, and smooth integration into existing production pipelines.
 
 ## Usage