Qwen2.5 is the latest iteration in the Qwen series of large language models developed by Alibaba Cloud’s Dev Team. Released on September 19, 2024, this series builds upon its predecessor, Qwen2, with significant enhancements in model architecture, training data, and overall performance

Developed by Alibaba Cloud, Qwen2 represents the next phase in the firm’s Tongyi Qianwen (Qwen) model series, which includes Tongyi Qianwen LLM (Qwen), the vision AI model Qwen-VL, and Qwen-Audio.

The Qwen model series comes pre-trained on multilingual data across diverse industries and domains, with Qwen-72B being the most robust model in the lineup. This model has been trained on a remarkable 3 trillion tokens of data. In comparison, Meta’s most potent Llama-2 variant is built on 2 trillion tokens. Meanwhile, Llama-3 is currently processing 15 trillion tokens.

These updated features have placed it in the top position on the Open LLM Leaderboard on the collaborative artificial intelligence platform Hugging Face, where it can be used for commercial or research activities.

Model Information

Qwen2 is a series of language models that includes decoder models of various sizes. Alibaba has released base language models and aligned chat models for each size. These models are built on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, a mixture of sliding window attention and full attention, among other features. Furthermore, Alibaba has developed an enhanced tokenizer that is adaptive to multiple natural languages and codes.

• Model sizes: Qwen2–0.5B, Qwen2–1.5B, Qwen2–7B, Qwen2–57B-A14B, and Qwen2–72B;
• Trained on data in 27 languages beyond English and Chinese;
• Outstanding results in various benchmarks;
• Enhanced coding and math capabilities;
• Context length support extended to 128K tokens with Qwen2–7B-
• Instruct and Qwen2–72B-Instruct.

Model Performance

Qwen2.5

Qwen2.5 demonstrates significant advancements in language model performance across various sizes. The flagship model, Qwen2.5-72B—a 72-billion-parameter dense decoder-only language model—has been benchmarked against leading open-source models like Llama-3.1-70B and Mistral-Large-V2. Results from instruction-tuned versions across multiple benchmarks show that Qwen2.5-72B excels in both model capabilities and alignment with human preferences. Notably, the base version of Qwen2.5-72B achieves top-tier performance, even outperforming larger models such as Llama-3-405B.

Qwen-Plus

The latest API-based model, has been compared to leading proprietary and open-source models, including GPT4-o, Claude-3.5-Sonnet, Llama-3.1-405B, and DeepSeek-V2.5. The benchmarking reveals that Qwen-Plus significantly outperforms DeepSeek-V2.5 and demonstrates competitive performance against Llama-3.1-405B. While it underperforms compared to GPT4-o and Claude-3.5-Sonnet in some areas, this assessment highlights Qwen-Plus’s strengths and identifies opportunities for future improvement, reinforcing a commitment to continuous innovation.

There is a notable industry shift towards Small Language Models (SLMs), with the performance gap between SLMs and larger language models (LLMs) rapidly diminishing. Remarkably, models with just 3 billion parameters are now achieving highly competitive results. Qwen2.5-3B stands out as a prime example, delivering impressive performance with approximately 3 billion parameters, showcasing its efficiency and capability compared to earlier models. This trend underscores the accelerated growth in knowledge density among language models and highlights the potential of smaller, more efficient architectures.

Qwen2.5-Coder

Building upon the success of CodeQwen1.5—which attracted numerous users for tasks such as debugging, answering coding-related questions, and providing code suggestions—the latest iteration, Qwen2.5-Coder, is specifically designed for coding applications. Performance results of Qwen2.5-Coder-7B-Instruct have been benchmarked against leading open-source models, including those with significantly larger parameter sizes. Despite its smaller size, Qwen2.5-Coder outperforms many larger language models across a range of programming languages and tasks, demonstrating exceptional coding capabilities and making it an excellent choice as a personal coding assistant.

Qwen2-Math

Following the release of the initial Qwen2-Math models last month, Qwen2.5-Math introduces significant enhancements. Compared to its predecessor, Qwen2.5-Math has been pretrained on a larger scale of math-related data, including synthetic data generated by Qwen2-Math. Additionally, support for Chinese has been extended, and reasoning capabilities have been strengthened by incorporating abilities to perform Chain-of-Thought (CoT), Proof-of-Thought (PoT), and Tree-of-Thought (TIR). The general performance of Qwen2.5-Math-72B-Instruct surpasses both Qwen2-Math-72B-Instruct and GPT4-o. Even smaller expert models like Qwen2.5-Math-1.5B-Instruct achieve highly competitive performance against large language models, showcasing efficiency and advanced mathematical reasoning capabilities.

Qwen 2.5 vs Other AI Models

The above image presents a comparison of various language models across three evaluation metrics: Artificial Analysis Quality Index, Reasoning & Knowledge (MMLU), and Quantitative Reasoning (MATH).

• Artificial Analysis Quality Index: The model Qwen2.5-72B scores 75, slightly outperforming models like Mistral Large (73) and Llama 3.1-70B (65), but trailing behind models like GPT-46 AQL v2 (82) and Claude 3.5 Sonnet (77).
• Reasoning & Knowledge (MMLU): Qwen2.5-72B achieves an 86% score, matching Mistral Large and Claude 3.5 Sonnet, while outperforming Llama 3.1-70B (82%) but falling slightly short of GPT-46 AQL v2 (89%).
• Quantitative Reasoning (MATH): Qwen2.5-72B performs strongly with an 81% score, second only to 07-mini (90%), and surpassing models like Claude 3.5 Sonnet (75%) and Llama 3.1-70B (60%).

While Qwen2.5-72B demonstrates strong performance across several benchmarks, it is essential to understand the specific areas where it excels and where it falls short compared to its competitors. For instance, Qwen2.5-72B’s superior performance in Quantitative Reasoning highlights its exceptional capability in mathematical problem-solving, making it highly suitable for tasks requiring rigorous logical reasoning and computation. On the other hand, its slightly lower scores in other areas, such as general reasoning and knowledge, compared to models like GPT4-o, suggest opportunities for further enhancement in these domains. By analyzing these differences, we can better appreciate the targeted strengths of Qwen2.5-72B and identify potential areas for future refinement to make it an even more versatile AI model.

Looking Ahead: The Future of AI Language Models

The release of Alibaba Cloud’s Qwen 2.5 series marks a significant milestone in the ongoing evolution of AI technology. This advancement not only sets a new benchmark for large language models but also underscores the rapid pace of innovation in artificial intelligence. As we witness improvements from one version to the next, it’s clear that we are still in the early stages of realizing the full potential of these powerful models.

Future developments are likely to focus on enhancing reasoning capabilities, enabling more nuanced and context-aware responses, and fostering seamless integration with other AI technologies like computer vision and speech recognition. Such progress will open the door to an explosion of innovative applications across various sectors—from education and research to business and the creative industries.

For those interested in exploring the foundational technologies behind advancements like Qwen 2.5, you might find these previous articles helpful:

• Exploring Transformer Architecture: A Comprehensive Guide delves into the groundbreaking model that has revolutionized natural language processing.
• Understanding GraphRAG: The Future of Retrieval-Augmented Generation in AI offers insights into advanced methods for improving AI’s information retrieval capabilities.

As we continue to explore and expand the capabilities of these models, we’re poised to uncover new possibilities and solutions to complex problems once thought insurmountable. This journey not only pushes the boundaries of what’s possible but also sets the stage for AI to transform society in profound and meaningful ways.