Qwen-2.5: Alibaba's Breakthrough in Open-Source AI
A Comprehensive Analysis of Next-Generation Language Models
January 29, 2025
Key Points
In a significant advancement for open-source AI development, Alibaba Cloud has introduced Qwen-2.5, a comprehensive suite of large language models that represents a substantial leap forward in capabilities and performance. This latest iteration builds upon previous versions with expanded knowledge, enhanced capabilities, and specialized variants for specific applications.
Model Overview and Technical Specifications
Qwen-2.5 represents a family of dense, decoder-only language models available in multiple sizes, ranging from 0.5B to 72B parameters. The models have been trained on an expansive dataset of 18 trillion tokens, significantly expanding their knowledge base and capabilities.
The model family includes:
Technical Capabilities
The models boast impressive technical specifications:
The 72B parameter version's architecture includes:
Performance and Benchmarks
Qwen-2.5 has demonstrated exceptional performance across various benchmarks, particularly in its 72B parameter version. The model has achieved several notable accomplishments:
Recent benchmark results show impressive scores across various domains:
Key Improvements and Features
Compared to its predecessors, Qwen-2.5 brings several significant improvements:
Enhanced Capabilities
Specialized Variants
Qwen2.5-Math
The mathematics-focused variant has shown particularly impressive results, with the 72B parameter version achieving 84% on the MATH Benchmark, outperforming competitors including GPT-4o, Claude 3.5 Sonnet, and Google's Math-Gemini Specialized 1.5 Pro.
Qwen2.5-Coder
The coding-specific variant offers:
Real-World Applications and Adoption
Qwen-2.5's impact is evident in its widespread adoption across industries. Over 90,000 enterprise deployments have been recorded through Alibaba Cloud's Model Studio platform, with notable implementations including:
Consumer Electronics
Xiaomi has integrated Qwen models into their AI assistant, Xiao Ai, enabling:
Gaming Industry
Perfect World Games has implemented Qwen for:
Development Tools
The Tongyi Lingma AI coding assistant, powered by Qwen2.5-coder, offers:
Infrastructure and Deployment
Alibaba Cloud has developed comprehensive infrastructure support for Qwen-2.5, including:
API Access
The models are available through various providers:
Deployment Options
Limitations and Ethical Considerations
Despite its impressive capabilities, Qwen-2.5 faces several challenges:
Technical Limitations
Ethical Concerns
Future Developments
Recent developments indicate continued evolution of the Qwen platform:
Visual Capabilities
The release of Qwen2.5-VL brings:
Process Reward Models
The introduction of Qwen2.5-Math-PRM series demonstrates:
Market Position and Competition
Qwen-2.5 has established itself as a significant player in the AI model landscape:
Competitive Advantages
Market Impact
Conclusion
Qwen-2.5 represents a significant advancement in open-source AI development, offering competitive performance against proprietary models while maintaining accessibility and versatility. Its comprehensive range of models, from lightweight to heavyweight variants, along with specialized versions for coding and mathematics, positions it as a versatile solution for various AI applications. While facing typical AI challenges regarding bias and ethical considerations, its strong adoption rate and continuous development suggest a promising future in the evolving AI landscape.
The model's success in both benchmarks and real-world applications demonstrates the growing capability of open-source AI models to compete with proprietary solutions, potentially democratizing access to advanced AI capabilities. As development continues, particularly in areas like visual understanding and process reward models, Qwen-2.5 appears poised to maintain its position as a leading open-source AI solution.
Sources
Alibaba Cloud Community
This article introduces the latest addition to the Qwen family, Qwen2.5, along with specialized models for coding and mathematics.
Our latest release features the LLMs Qwen2.5, along with specialized models for coding, Qwen2.5-Coder, and mathematics, Qwen2.5-Math. All open-weight models are dense, decoder-only language models, available in various sizes, including: Qwen2.5: 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B, Qwen2.5-Coder: 1.5B, 7B, and 32B on the way, Qwen2.5-Math: 1.5B, 7B, and 72B.
In terms of Qwen2.5, the language models, all models are pretrained on our latest large-scale dataset, encompassing up to 18 trillion tokens. Compared to Qwen2, Qwen2.5 has acquired significantly more knowledge (MMLU: 85+) and has greatly improved capabilities in coding (HumanEval 85+) and mathematics (MATH 80+). Additionally, the new models achieve significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON.
Like Qwen2, the Qwen2.5 language models support up to 128K tokens and can generate up to 8K tokens. They also maintain multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
Qwen Team
An introduction to Qwen2.5-Max, a large-scale MoE model pretrained on over 20 trillion tokens with SFT and RLHF methodologies.
It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in effectively scaling extremely large models, whether they are dense or Mixture-of-Expert (MoE) models. Many critical details regarding this scaling process were only disclosed with the recent release of DeepSeek V3. Concurrently, we are developing Qwen2.5-Max, a large-scale MoE model that has been pretrained on over 20 trillion tokens and further post-trained with curated Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) methodologies.
We evaluate Qwen2.5-Max alongside leading models, whether proprietary or open-weight, across a range of benchmarks that are of significant interest to the community. These include MMLU-Pro, which tests knowledge through college-level problems, LiveCodeBench, which assesses coding capabilities, LiveBench, which comprehensively tests the general capabilities, and Arena-Hard, which approximates human preferences.
Qwen2.5-Max outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro.
GitHub
Qwen2.5 is the large language model series developed by Qwen team, Alibaba Cloud.
领英推荐
All our open-source models, except for the 3B and 72B variants, are licensed under Apache 2.0. You can find the license files in the respective Hugging Face repositories. The models demonstrate significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. Qwen2.5 models are generally more resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Qwen2.5 has been pretrained on our latest large-scale dataset, encompassing up to 18 trillion tokens. Context length support up to 128K tokens and can generate up to 8K tokens. Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
In the past three months since Qwen2's release, numerous developers have built new models on the Qwen2 language models, providing us with valuable feedback. During this period, we have focused on creating smarter and more knowledgeable language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5. Dense, easy-to-use, decoder-only language models, available in 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B sizes, and base and instruct variants. Pretrained on our latest large-scale dataset, encompassing up to 18T tokens. Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots. Context length support up to 128K tokens and can generate up to 8K tokens. Multilingual support for over 29 languages.
2024.09.19: We released the Qwen2.5 series. This time there are 3 extra model sizes: 3B, 14B, and 32B for more possibilities. 2024.06.06: We released the Qwen2 series. 2024.03.28: We released the first MoE model of Qwen: Qwen1.5-MoE-A2.7B! Temporarily, only HF transformers and vLLM support the model. We will soon add the support of llama.cpp, mlx-lm, etc. 2024.02.05: We released the Qwen1.5 series.
Alibaba Cloud
The article introduces Alibaba Cloud's open-source Qwen 2.5-72B-Instruct has achieved the top position on the OpenCompass large language model leaderboard.
According to its latest September update, Alibaba Cloud's open-source Qwen 2.5-72B-Instruct has claimed the top spot on the OpenCompass large language model leaderboard. In various benchmarks, it surpasses even closed-source SOTA models, such as Claude 3.5 and GPT-4o.
Qwen 2.5-72B-Instruct showcased strong overall capabilities, achieving the highest score of 74.2 in coding and an impressive 77 in mathematics, outperforming Claude 3.5 (72.1) and GPT-4o (70.6). In a recent article, OpenCompass commended Qwen 2.5 as its first-ever open-source champion, reflecting the rapid progress in the open-source LLM community.
TIMETOACT GROUP
The TIMETOACT GROUP LLM Benchmarks highlight the most powerful AI language models for digital product development. Discover which large language models performed best in September.
According to the latest benchmarks, GPT o1-preview models are the best performing, with Gemini 1.5 Pro v002 taking 3rd place. Qwen 2.5 72B Instruct achieved strong performance with scores of 79 for code, 92 for CRM, 94 for docs, 100 for integration, 71 for marketing, 59 for reasoning, and an overall score of 83.
Medium
An analysis of Alibaba Cloud's latest iteration of their advanced large language model, comparing its capabilities with GPT-4o and other models.
The 72B parameter model, Qwen 2.5–72B, outperforms leading open-source models like Llama 2 70B and Mistral-Large-V2 in several instruction-tuned evaluations. Even the smaller Qwen 2.5–3B model achieves impressive performance, showcasing its efficiency and capability. Qwen 2.5-Coder also outperforms many larger language models in coding tasks, making it a powerful tool for developers.
So to finally answer the question, Qwen 2.5 generally performs well but is outmatched by GPT-4o in certain benchmarks, particularly in coding tasks and overall speed. But overall for an open-source model, Qwen 2.5 is quite impressive.
Alibaba Cloud
The MaaS Pioneer Upgrades its AI Development Platform, Unveils Enhanced Propriety LLM Model, and Expands Open-source Offerings to Cater for Soaring Generative AI Demand.
Since June last year, the Qwen family has attracted over 90,000 enterprise deployments through Alibaba Cloud's generative AI platform, Model Studio, further demonstrating its leadership position backed by robust adoption across industries from consumer electronics, automobiles to gaming, making Qwen one of the most sought-after LLMs in China.
Xiaomi, a leader in consumer electronics and smart manufacturing, has integrated Alibaba Cloud's models into its AI assistant, Xiao Ai, fueling features such as image generation and comprehension across its latest smartphone range and the smart electric vehicle. This integration empowers Xiao Ai to generate images on the car infotainment system simply through voice commands, offering passengers an enriched in-vehicle experience with interactive entertainment options.
Perfect World Games, a Chinese gaming company, has integrated Alibaba Cloud's Qwen into game development. The combination of cloud and AI capabilities has produced positive effects in multiple areas of game development, including plot, dialogue, audio and animation generation. Looking ahead, the two will deepen collaborations in game elements such as AI non-player character (NPC), real-time content generation, to jointly explore AI in Gameplay.
Alizila
The company launched 100 open-sourced Qwen2.5 multimodal models and a text-to-video AI solution. Alibaba Cloud announced significant upgrades to its AI infrastructure services to maximize customer value.
The new model has significantly more knowledge and greatly improved coding and mathematics capabilities, and is better at instruction following, long text generation, understanding structured data and generating structured outputs. Additionally, Alibaba Cloud is advancing its Tongyi large model family with a new text-to-video model, and an enhanced large vision language model.
VentureBeat
If you haven't heard of 'Qwen2' it's understandable, but that should all change starting today with a surprising new release taking the crown from all others when it comes to a very important subject in software development, engineering, and STEM fields the world over: math.
Today, Alibaba Cloud's Qwen team peeled off the wrapper on Qwen2-Math, a new 'series of math-specific large language models' designed for the English language. The most powerful of these outperform all others in the world — including the vaunted OpenAI GPT-4o, Anthropic Claude 3.5 Sonnet, and even Google's Math-Gemini Specialized 1.5 Pro. Specifically, the 72-billion parameter Qwen2-Math-72B-Instruct variant clocks in at 84% on the MATH Benchmark for LLMs, which provides 12,500 'challenging competition mathematics problems.'
Hugging Face
Qwen2.5 is the latest series of Qwen large language models with improvements in coding, mathematics, instruction following and long text generation capabilities.
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
Technical specifications: Type: Causal Language Models, Training Stage: Pretraining, Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias, Number of Parameters: 72.7B, Number of Paramaters (Non-Embedding): 70.0B, Number of Layers: 80, Number of Attention Heads (GQA): 64 for Q and 8 for KV, Context Length: 131,072 tokens
Medium
An analysis of Qwen2.5's capabilities and performance in various tasks
Trained on an expansive dataset of 18 trillion tokens, Qwen2.5 significantly improves its capabilities in general knowledge, coding proficiency, and mathematical reasoning. With support for multilingual tasks across more than 29 languages, Qwen2.5 models excel at generating long-form texts, following complex instructions, and managing structured data seamlessly.
Inferless
A comprehensive guide to understanding and implementing Qwen models, including their evolution, features, and deployment options
The Qwen2.5 series expanded the training dataset to 18 trillion tokens and introduced cost-effective models like Qwen2.5-14B and Qwen2.5-32B. A mobile-friendly Qwen2.5-3B was also released. They have also released Qwen2.5-Math and Qwen2.5-Coder. Qwen2.5 showed improved performance in coding, math, and instruction following.
Qwen AI
A comprehensive analysis of Qwen 2.5's numerous strengths and potential limitations, offering a balanced perspective on its capabilities, applications, and areas for improvement.
Potential for Bias and Ethical Concerns: Like many AI models, Qwen 2.5 may inherit biases present in its training data. Addressing these biases and ensuring ethical use of the model remains an ongoing challenge for developers and users alike.
Are there any ethical concerns with using Qwen 2.5? Like all AI models, Qwen 2.5 may inherit biases from its training data. Users should be aware of potential biases and implement appropriate safeguards. It's also important to consider privacy implications, especially when handling sensitive data. Responsible use and regular auditing of outputs are recommended to ensure ethical deployment of the model.
Medium
An analysis of how Qwen represents China's technological ambition and strategic approach to artificial intelligence.
Alibaba has taken steps towards responsible AI development with Qwen, focusing on transparency in model development, built-in content moderation, cultural sensitivity mechanisms, and privacy protection protocols. However, like all AI models, Qwen has its limitations. It occasionally suffers from hallucinations, where it generates plausible but incorrect information. There's also the potential for bias in complex reasoning tasks, and its knowledge base might not always reflect real-time updates, which can be a limitation in rapidly changing fields.
Carnegie Endowment for International Peace
The AI race is breaking open. An upcoming summit offers an opportunity to U.S. and Chinese companies to agree on safety and security measures.
Despite growing global concern around large-scale risks, the U.S. and Chinese governments have made little progress on a bilateral agreement to regulate frontier AI. But a surprising consensus among leading AI developers in both countries around the need for safeguards has quietly emerged, including DeepSeek. Last month, DeepSeek joined sixteen other Chinese companies in signing onto the Artificial Intelligence Safety Commitments (人工智能安全承诺). While branded as a domestic Chinese initiative, the commitments bear strong similarity to ongoing global industry-led efforts to put safeguards in place for frontier AI piloted at last year's AI Summit in Seoul, known as the Seoul Commitments.
APIpie
The Qwen Series represents a comprehensive family of transformer-based models optimized for a wide range of NLP applications.
The Qwen Series represents a comprehensive family of transformer-based models optimized for a wide range of NLP applications. Developed by Alibaba Cloud, these models leverage cutting-edge technology to deliver exceptional performance in conversational AI, instruction-following tasks, and extended-context interactions. The models are available through various providers integrated with APIpie's routing system. Key features include: Extended Token Capacity: All models support up to 32,768 tokens for efficient handling of long-text inputs and context-rich conversations. Multi-Provider Availability: Accessible across platforms like OpenRouter, EdenAI, Together, and Amazon Bedrock. Diverse Subtypes: Includes Chat, Instruction, and Vision-Language variants tailored for specific applications. Scalability: Models ranging from lightweight solutions (1.5B parameters) to high-capacity configurations (72B parameters) for advanced tasks.
Applications and Integrations: Conversational AI: Powering chatbots, virtual assistants, and other dialogue-based systems. Try it with LibreChat or OpenWebUI. Instructional Scenarios: Tailored for executing complex, multi-step tasks based on user inputs. Vision-Language Models: Addressing multimodal tasks combining textual and visual inputs using specialized VL models. Extended Context Tasks: Providing coherent responses for long-sequence inputs.
DataCamp
Learn about the Qwen2.5-Coder series by building an AI code review assistant using Qwen 2.5-Coder-32B-Instruct and Gradio.
The Qwen2.5-Coder series offers parameter variants ranging from 0.5B to 32B, providing us developers with the flexibility to experiment on both edge devices and heavy-load GPUs. The Qwen2.5-Coder series (formerly known as CodeQwen1.5), developed by Alibaba's Qwen research team, is dedicated to advancing Open CodeLLMs. The series includes models like the Qwen 2.5-32B-Instruct, which has become the state-of-the-art open-source code model, rivaling the coding capabilities of proprietary giants like GPT-4o and Gemini. These models are presented as being: Powerful: These models are capable of advanced code generation, repair, and reasoning. Diverse: They support over 92 programming languages, including Python, Java, C++, Ruby, and Rust. Practical: Qwen 2.5 models are designed for real-world applications, from code assistance to artifact generation, with a long-context understanding of up to 128K tokens.
Alibaba Cloud
Alibaba Cloud has unveiled an expanded suite of large language models and AI development tools, upgraded infrastructure offerings, and new support programs for global developers at its annual developer summit today.
The newly released open-source Qwen 2.5 models, ranging from 0.5 to 72 billion parameters in size, feature enhanced knowledge and stronger capabilities in math and coding and are able to support over 29 languages, catering to a wide array of AI applications both at the edge or in the cloud across various sectors from automobile, gaming to science research.
Developers can also leverage Tongyi Lingma, Alibaba Cloud's proprietary AI coding assistant powered by the Qwen 2.5-coder model. The AI Programmer offers features such as code completion and optimization, debugging assistance, code snippet search and batch unit test generation. It provides developers with an efficient and seamless coding experience, significantly enhancing productivity and creativity.
TechCrunch
Chinese AI lab DeepSeek might be getting the bulk of the tech industry's attention this week. But one of its top domestic rivals, Alibaba, isn't sitting idly by.
Alibaba's Qwen team on Monday released a new family of AI models, Qwen2.5-VL, that can perform a number of text and image analysis tasks. The models can parse files, understand videos, and count objects in images, as well as control a PC — similar to the model powering OpenAI's recently launched Operator. Per the Qwen team's benchmarking, the best Qwen2.5-VL model beats OpenAI's GPT-4o, Anthropic's Claude 3.5 Sonnet, and Google's Gemini 2.0 Flash on a range of video understanding, math, document analysis, and question-answering evaluations.
Marktechpost
Mathematical reasoning has long been a significant challenge for Large Language Models (LLMs). Errors in intermediate reasoning steps can undermine both the accuracy and reliability of final outputs.
The Alibaba Qwen Team recently published a paper titled 'Lessons of Developing Process Reward Models in Mathematical Reasoning.' Alongside this research, they introduced two PRMs with 7B and 72B parameters, part of their Qwen2.5-Math-PRM series. These models address significant limitations in existing PRM frameworks, employing innovative techniques to improve the accuracy and generalization of reasoning models.
The Qwen2.5-Math-PRM models demonstrated strong results on PROCESSBENCH and other evaluation metrics. For example, the Qwen2.5-Math-PRM-72B model achieved an F1 score of 78.3%, surpassing many open-source alternatives. In tasks requiring step-wise error identification, it outperformed proprietary models like GPT-4-0806.
Alibaba Cloud
Alibaba Cloud unveils 100 open-sourced Qwen 2.5 multimodal models and new text-to-video AI model to bring visual creations to a higher level.
The cloud pioneer has also announced a slew of innovative updates to its full-stack AI infrastructure covering green datacenter architecture, data management, model training and inferencing. This includes Next-Gen Data Center Architecture for Surging AI Development, Open Lake Solution to Maximize Data Utility, AI Scheduler with Integrated Model Training and Inference, DMS for Unified Management of Metadata, and More Powerful Elastic Compute Service.
This detailed analysis was created with "Do Your Research" -- an AI-powered autonomous researcher. https://www.dhirubhai.net/posts/dmitry-shapiro-a2b1_introducing-do-your-research-a-new-ai-activity-7290438886802042880-TB32
Architecting the future… ?? + ?? = ?? ?? Co-Founder @ The Wai AI ?? | #girldad Building AI Agents ?? | Workflow Wizard ??
1 个月OpenAI: ‘We have the best models.’ Alibaba: ‘Ok, but what if everyone had them?
Building LLMs that write unreasonably well >< CEO & Founder at Qme.ai
1 个月The public is in a honeymoon phase with Deepseek now, it's hard to digest anything new at this point :))