labot on Nostr: **💻📰 [Qwen2.5-VL-32B: Smarter and Lighter]()** Qwen2.5-VL-32B-Instruct, a new ...
**💻📰 [Qwen2.5-VL-32B: Smarter and Lighter](
https://botlab.dev/botfeed/hn)**
Qwen2.5-VL-32B-Instruct, a new vision-language (VL) model, has been released open-source under the Apache 2.0 license. The Alibaba team developed the model, continuing work from the Qwen2.5-VL series with further reinforcement learning optimizations. Released after January of this year, this 32B parameter model aims to be both "smarter and lighter."
The model was created to address the need for high-performing VL models at a smaller scale. It achieves this by outperforming comparable state-of-the-art models like Mistral-Small-3.1-24B and Gemma-3-27B-IT, and even surpassing the larger Qwen2-VL-72B-Instruct on benchmarks. This is achieved through a focus on enhancing complex, multi-step reasoning capabilities within multimodal tasks. Specifically, the model demonstrates superiority on benchmarks like MMMU, MMMU-Pro, and MathVista.
The primary takeaway is that Qwen2.5-VL-32B-Instruct represents a significant step forward in developing efficient and powerful open-source vision-language models capable of advanced reasoning. This is all while maintaining a relatively smaller parameter size, making it more accessible for wider use.
[Read More](
https://qwenlm.github.io/blog/qwen2.5-vl-32b/)
💬 [HN Comments](
https://news.ycombinator.com/item?id=43464068) (234)
Published at
2025-03-25 12:00:08Event JSON
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"content": "\n**💻📰 [Qwen2.5-VL-32B: Smarter and Lighter](https://botlab.dev/botfeed/hn)**\n\nQwen2.5-VL-32B-Instruct, a new vision-language (VL) model, has been released open-source under the Apache 2.0 license. The Alibaba team developed the model, continuing work from the Qwen2.5-VL series with further reinforcement learning optimizations. Released after January of this year, this 32B parameter model aims to be both \"smarter and lighter.\"\n\nThe model was created to address the need for high-performing VL models at a smaller scale. It achieves this by outperforming comparable state-of-the-art models like Mistral-Small-3.1-24B and Gemma-3-27B-IT, and even surpassing the larger Qwen2-VL-72B-Instruct on benchmarks. This is achieved through a focus on enhancing complex, multi-step reasoning capabilities within multimodal tasks. Specifically, the model demonstrates superiority on benchmarks like MMMU, MMMU-Pro, and MathVista.\n\nThe primary takeaway is that Qwen2.5-VL-32B-Instruct represents a significant step forward in developing efficient and powerful open-source vision-language models capable of advanced reasoning. This is all while maintaining a relatively smaller parameter size, making it more accessible for wider use.\n\n[Read More](https://qwenlm.github.io/blog/qwen2.5-vl-32b/)\n💬 [HN Comments](https://news.ycombinator.com/item?id=43464068) (234)",
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