Qwen2.5-Math: Open-Source Mathematical LLM Series Released
Alibaba's Qwen team has released Qwen2.5-Math, an upgraded series of open-source mathematical LLMs including base and instruction-tuned models at 1.5B, 7B, and 72B parameter scales, plus a mathematical reward model. The models support Chain-of-Thought (CoT) and Tool-Integrated Reasoning (TIR) for English and Chinese math problem solving. This follows the Qwen2-Math release approximately one month prior and is claimed to be the leading open-source mathematical LLM series.
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Introducing Qwen2-Math: Math-Specialized LLMs from Alibaba's Qwen Team
Alibaba's Qwen team has released Qwen2-Math and Qwen2-Math-Instruct, a series of math-specialized large language models built on the Qwen2 architecture. The models are designed to enhance arithmetic and mathematical reasoning capabilities in LLMs. The initial release supports English only, with bilingual English/Chinese versions announced as forthcoming.
Introducing the Qwen Series: Overview of Alibaba's Open-Source LLM Journey
Alibaba's Qwen team published a retrospective introduction to the Qwen series of large language models, four months after the initial Qwen-7B open-source release. The post consolidates links to their paper, GitHub, Hugging Face, and ModelScope repositories, and outlines the team's objectives for the open-source LLM program. It serves as a canonical reference point for the Qwen model family's public positioning.
Qwen2.5-LLM: Alibaba releases open-weight language models from 0.5B to 72B
Alibaba's Qwen team releases the Qwen2.5 series of decoder-only dense language models, open-sourcing seven variants spanning 0.5B to 72B parameters. The release targets production use cases in the 10-30B range and mobile deployments at 3B scale. This represents a significant expansion of the open-weights frontier from a Tier 1 Chinese AI lab.
Qwen2 Model Family Released: Five Sizes, 128K Context, Multilingual
Alibaba's Qwen team has released Qwen2, an evolution from Qwen1.5, comprising five pretrained and instruction-tuned models ranging from 0.5B to 72B parameters, including a 57B mixture-of-experts variant (57B-A14B). The release highlights training on 27 additional languages beyond English and Chinese, significantly improved coding and mathematics performance, and extended context support up to 128K tokens for the 7B and 72B instruct variants. Benchmark results are claimed to be state-of-the-art across a large number of evaluations.
Qwen2.5: Large-Scale Open-Source Foundation Model Family Release
Alibaba's Qwen team has released Qwen2.5, described as potentially the largest open-source model release in history, following three months of development after Qwen2. The release encompasses a family of foundation models with improvements in knowledge and reasoning capabilities. The announcement targets developers who have been building on Qwen2 and incorporates feedback from that community.
Qwen2.5-Math Process Reward Model for Mathematical Reasoning Supervision
Alibaba's Qwen team introduces a process reward model (PRM) aimed at improving the reliability of mathematical reasoning in LLMs by supervising intermediate reasoning steps rather than only final answers. The work addresses the problem of models producing plausible but flawed intermediate derivations even when reaching correct conclusions. The release includes model weights on HuggingFace and ModelScope alongside a GitHub repository.
Qwen2.5-Max: Large-Scale MoE Model Release by Alibaba's Qwen Team
Alibaba's Qwen team announces Qwen2.5-Max, a large-scale Mixture-of-Experts language model. The post acknowledges that scaling insights for very large MoE models have been limited, citing DeepSeek V3's recent disclosures as a reference point. The model is positioned as a frontier-scale MoE system developed concurrently with ongoing Qwen2 research.
Qwen2.5-Coder Series Open-Sourced: 32B Model Claims SOTA, Matches GPT-4o on Coding
Alibaba's Qwen team has open-sourced the Qwen2.5-Coder family of code-specialized language models, with the flagship 32B-Instruct variant claiming state-of-the-art performance among open-source code models and parity with GPT-4o on coding benchmarks. The release spans multiple model sizes, expanding on previously released smaller variants. The models are described as combining strong coding ability with general reasoning and mathematical skills.