News from the AI & ML world
DeeperML
Alexey Shabanov@TestingCatalog
//
Alibaba's Qwen team has launched Qwen3, a new family of open-source large language models (LLMs) designed to compete with leading AI systems. The Qwen3 series includes eight models ranging from 0.6B to 235B parameters, with the larger models employing a Mixture-of-Experts (MoE) architecture for enhanced performance. This comprehensive suite offers options for developers with varied computational resources and application requirements. All the models are released under the Apache 2.0 license, making them suitable for commercial use.
The Qwen3 models boast improved agentic capabilities for tool use and support for 119 languages. The models also feature a unique "hybrid thinking mode" that allows users to dynamically adjust the balance between deep reasoning and faster responses. This is particularly valuable for developers as it facilitates efficient use of computational resources based on task complexity. Training involved a large dataset of 36 trillion tokens and was optimized for reasoning, similar to the Deepseek R1 model.
Benchmarks indicate that Qwen3 rivals top competitors like Deepseek R1 and Gemini Pro in areas like coding, mathematics, and general knowledge. Notably, the smaller Qwen3–30B-A3B MoE model achieves performance comparable to the Qwen3–32B dense model while activating significantly fewer parameters. These models are available on platforms like Hugging Face, ModelScope, and Kaggle, along with support for deployment through frameworks like SGLang and vLLM, and local execution via tools like Ollama and llama.cpp.
ImgSrc: www.testingcata
References :
The Qwen3 models boast improved agentic capabilities for tool use and support for 119 languages. The models also feature a unique "hybrid thinking mode" that allows users to dynamically adjust the balance between deep reasoning and faster responses. This is particularly valuable for developers as it facilitates efficient use of computational resources based on task complexity. Training involved a large dataset of 36 trillion tokens and was optimized for reasoning, similar to the Deepseek R1 model.
Benchmarks indicate that Qwen3 rivals top competitors like Deepseek R1 and Gemini Pro in areas like coding, mathematics, and general knowledge. Notably, the smaller Qwen3–30B-A3B MoE model achieves performance comparable to the Qwen3–32B dense model while activating significantly fewer parameters. These models are available on platforms like Hugging Face, ModelScope, and Kaggle, along with support for deployment through frameworks like SGLang and vLLM, and local execution via tools like Ollama and llama.cpp.
ImgSrc: www.testingcata
Share:
References :
- pub.towardsai.net: TAI #150: Qwen3 Impresses as a Robust Open-Source Contender
- gradientflow.com: Table of Contents Model Architecture and Capabilities What is Qwen 3 and what models are available in the lineup? What are the “Hybrid Thinking Modes†in Qwen 3, and why are they valuable for developers?
- THE DECODER: An article about Qwen3 series from Alibaba debuts with benchmark results matching top competitors
- TestingCatalog: Reporting on Alibaba Cloud debuting 235B-parameter Qwen 3 to challenge US model dominance
- Towards AI: TAI #150: Qwen3 Impresses as a Robust Open-Source Contender
- www.analyticsvidhya.com: Qwen3 Models: How to Access, Performance, Features, and Applications
- RunPod Blog: Qwen3 Released: How Does It Stack Up?
- bdtechtalks.com: Alibaba’s Qwen3: Open-weight LLMs with hybrid thinking | BDTechTalks
- AI News | VentureBeat: Alibaba launches open source Qwen3 model that surpasses OpenAI o1 and DeepSeek R1
- the-decoder.com: Qwen3 series from Alibaba debuts with benchmark results matching top competitors