Pro/Qwen/Qwen2.5-7B-Instruct

Qwen2.5 is a general-purpose large language model series launched by Alibaba, featuring "ultra-long context support of 128K tokens" and "structured data processing capabilities." With a deeply optimized inference architecture, it provides industry-leading solutions for handling complex tasks.

• Comprehensive performance leadership: The flagship model Qwen2.5-72B outperforms competitors such as Llama-3.1-70B and Mistral-Large-V2 in benchmark tests including programming (LiveCodeBench 55.5), mathematics (MATH 83.1), and general abilities (MMLU-Pro 71.1).
• Ultra-long context support: Natively supports a context of 128K tokens, and with YaRN technology, this can be extended to 131K tokens, making it easy to handle ultra-long text tasks.
• Structured data processing: Significantly enhances the ability to understand structured data such as tables, supports JSON-formatted output, and is well-suited for enterprise-level data interaction scenarios.
• Multi-language coverage: Supports over 29 languages (including Chinese, English, French, Spanish, Arabic, and more), with localized optimizations that improve cross-language understanding accuracy.
• Open-source and open: The entire series of models has been open-sourced on Hugging Face and ModelScope Community, offering API access and local deployment options, and supporting mainstream frameworks such as SGLang and vLLM.

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Core Capabilities

🚀 Performance leap: Outperforms top competitors in programming, mathematics, and multi-language tasks, setting a new benchmark for enterprise-level AI applications.

📏 Ultra-long text processing: Natively supports a context of 128K tokens, which can be extended to 131K tokens via YaRN technology.

🛠️ Structured output: Supports JSON-formatted output, making it easy to parse structured data such as tables and enhancing enterprise data interaction efficiency.

🌐 Multi-language expert: Deeply optimized for low-resource languages such as Chinese and Arabic, improving cross-language understanding accuracy by 15%.

⚡ Cost-effective architecture: Achieves “large-scale strength with high performance” using 72 billion parameters, reducing the entry barrier for enterprise AI applications by 60%.