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zen-blog/blog/qwen1.5-32b.txt

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12:["$","p",null,{"children":"Qwen1.5-32B is a new member of the Qwen1.5 language model series, and besides model sizes, there is almost nothing different in model architecture except for the inclusion grouped query attention (GQA). Thus it has better potential of more efficient inference performance in model serving."}]
13:["$","p",null,{"children":"Here we provide the performance comparison with the SOTA of around 30B parameters or larger model sizes, in terms of the base capability evaluation, chat evaluation, and multilingual evaluation. Below, we report the evaluation of capabilities of base language models:"}]
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15:["$","p",null,{"children":"Our 32B model demonstrates competitive performance across a variety of tasks, including MMLU, GSM8K, HumanEval, and BBH. Compared with the 72B parameter model, Qwen1.5-32B exhibits a slight decrease in performance, yet it still outperforms other 30B models, such as Llama2-34B and Mixtral-8x7B, in most tasks."}]
16:["$","p",null,{"children":"In terms of the chat models, we follow the evaluation recipe of Qwen1.5 to test their performance on MT-Bench and Alpaca-Eval 2.0. The results are shown below:"}]
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18:["$","p",null,{"children":"Significantly, Qwen1.5-32B-Chat achieves a score of over 8 points, and the gap between Qwen1.5-32B-Chat and Qwen1.5-72B-Chat is relatively small. This result indicates that the 32B model is a viable alternative for users who require a more efficient and cost-effective solution for chat applications."}]
19:["$","p",null,{"children":"We also test the multilingual capabilities of Qwen1.5-32B on a diverse set of 12 languages, including Arabic, Spanish, French, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Indonesian, covering domains including exams, understanding, math, and translation. Results are shown below:"}]
1a:["$","p",null,{"children":"The detailed results are demonstrated below:"}]
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1c:["$","p",null,{"children":"Similar to other Qwen1.5 models, the 32B one also has decent multiplingual capabilities and it is also slightly behind the 72B model."}]
1d:["$","p",null,{"children":"Finally we come to take a look at its performance in the long-context evaluation, Needle in a Haystack. We are happy to see that it is able to achieve a top-level performance in the context of 32K tokens."}]
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1f:["$","p",null,{"children":["We advise you to read our blog for ",["$","$Lf",null,{"href":"https://qwenlm.github.io/blog/qwen1.5/","children":"Qwen1.5"}]," to figure out the usages with Transformers, vLLM, llama.cpp, Ollama, etc."]}]
20:["$","h1",null,{"className":"flex scroll-m-28 flex-row items-center gap-2","id":"conclusion","children":[["$","a",null,{"data-card":"","href":"#conclusion","className":"peer","children":"Conclusion"}],["$","svg",null,{"ref":"$undefined","xmlns":"http://www.w3.org/2000/svg","width":24,"height":24,"viewBox":"0 0 24 24","fill":"none","stroke":"currentColor","strokeWidth":2,"strokeLinecap":"round","strokeLinejoin":"round","className":"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100","aria-hidden":true,"children":[["$","path","1cjeqo",{"d":"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"}],["$","path","19qd67",{"d":"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"}],"$undefined"]}]]}]
21:["$","p",null,{"children":"We release the medium-size model Qwen1.5-32B as well as its chat counterpart. The models require much less memory footprint and run significantly faster than the 72B model. We hope that this release can help our users to figure out a better solution for their downstream application to tackle the problems of weak capabilities of 14B models (especially in agent playing scenarios) and high inference costs of 72B models."}]
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