Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.
Official3
qwenlm.github.io2025-03-24authorityHN front pageabout the Qwen 2.5 familyHN 544 pts · 293 comments606 words
Alibaba open-sourced Qwen2.5-VL-32B-Instruct under the Apache 2.0 license, optimizing the model with reinforcement learning to improve human alignment and mathematical reasoning. This 32B parameter model outperforms larger 72B predecessors on benchmarks like MM-MT-Bench and MMMU-Pro while maintaining top-tier pure text capabilities. The model operates within the paradigm of "fast thinking" to prioritize subjective experience and complex multi-step reasoning.written, reviewed, cited
- The model achieves significant advantages in MMMU, MMMU-Pro, and MathVista benchmarks focusing on complex, multi-step reasoning.
- Qwen2.5-VL-32B-Instruct outperforms its predecessor Qwen2-VL-72B-Instruct by a substantial margin on the MM-MT-Bench benchmark.
qwenlm.github.io2024-09-18authorityHN front pageabout the Qwen 2.5 familyHN 168 pts · 38 comments1,744 words
Qwen2.5 introduces dense decoder-only models pretrained on up to 18 trillion tokens, available in sizes from 0.5B to 72B parameters. Most variants are licensed under Apache 2.0, excluding the 3B and 72B models, and support a 128K context window with 8K generation limits. Specialized versions for coding and mathematics offer enhanced performance on benchmarks like HumanEval and MATH.written, reviewed, cited
- Qwen2.5-Coder-7B-Instruct outperforms many larger language models across a range of programming languages and tasks.
- Qwen2.5-Math-72B-Instruct general performance surpasses both Qwen2-Math-72B-Instruct and GPT4-o.
qwenlm.github.io2025-01-28authorityHN front pageabout the Qwen 2.5 familyHN 118 pts · 30 comments556 words
Alibaba Cloud releases Qwen2.5-Max, a large-scale MoE model pretrained on over 20 trillion tokens and post-trained with SFT and RLHF. The model outperforms DeepSeek V3 in Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond benchmarks. Its API, named qwen-max-2025-01-25, is OpenAI-API compatible and available via Alibaba Cloud Model Studio.written, reviewed, cited
Analysis7
simonwillison.net2024-11-13authorityHN front pageabout the Qwen 2.5 familyHN 164 pts · 65 comments635 words
Alibaba released the Qwen2.5-Coder-32B-Instruct model, an Apache 2.0 licensed open-source LLM. The 32B model runs on a 64GB MacBook Pro M2 via Ollama or MLX, fetching a 20GB quantized file. On MLX, it achieved 10.016 tokens-per-sec generation speed with 32.685 GB peak memory usage.written, reviewed, cited
- The model requires a workaround for an SSL bug when using the standard library instead of requests or httpx.
- Running the Ollama version was too slow because the author did not have the tool compiled to use the Mac's GPU.
github.com2024-11-09authorityabout the Qwen 2.5 familyHN 21 pts · 6 comments1,815 words
qwenlm.github.io2024-11-11authorityabout the Qwen 2.5 familyHN 23 pts · 0 comments1,210 words
alizila.com2025-03-27about the Qwen 2.5 familyHN 29 pts · 3 comments
qwenlm.github.io2025-01-27authorityabout the Qwen 2.5 familyHN 8 pts · 0 comments
simonwillison.net2025-03-25authorityabout the Qwen 2.5 familyHN 3 pts · 2 comments
medium.com2025-01-29about the Qwen 2.5 familyHN 17 pts · 4 comments