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Advancements in the Qwen2.5 Language Models

Qwen2.5-LLM: Extending the boundary of LLMs ๐Ÿ”—

GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction In this blog, we delve into the details of our latest Qwen2.5 series language models. We have developed a range of decoder-only dense models, with seven of them open-sourced, spanning from 0.5B to 72B parameters. Our research indicates a significant interest among users in models within the 10-30B range for production use, as well as 3B models for mobile applications. To meet these demands, we are open-sourcing Qwen2.

The Qwen2.5 series of language models represents a significant advancement in natural language processing, with models ranging from 0.5B to 72B parameters. Key enhancements include an expanded pre-training dataset, improved performance across various benchmarks, and better coding and math capabilities. Models such as Qwen2.5-3B, 14B, and 32B specifically target mobile and production applications, while the Qwen2.5-72B model excels in general tasks and instruction following. The series emphasizes open-source availability and significant improvements over its predecessor models, making it a competitive option in the field.

What are the key improvements in the Qwen2.5 models compared to their predecessors?

The Qwen2.5 models feature an expanded dataset for training, improved capabilities in coding and mathematics, and better alignment with human preferences in response generation.

Which new models are included in the Qwen2.5 series?

The series introduces Qwen2.5-3B, Qwen2.5-14B, and Qwen2.5-32B, aimed at mobile applications and production use, alongside the larger Qwen2.5-72B model.

How do the Qwen2.5 models perform on benchmark evaluations?

The Qwen2.5 models demonstrate significant performance improvements across various benchmarks, particularly in natural language understanding, coding, and math tasks, often surpassing previous models and competitors.

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