Instruction-tuned 32B Qwen2.5
According to the model card, this is the instruction-tuned 32B Qwen2.5 model covering pretraining and post-training stages, within a series ranging from 0.5B to 72B parameters.
Open Source Model Profile · Qwen
Qwen2.5-32B-Instruct is a 32.76B-parameter Qwen2 instruction-tuned text-generation model from Qwen with documented 128K-token context support.
Qwen2.5-32B-Instruct is published by Qwen as an instruction-tuned Qwen2 text-generation model. The captured configuration identifies Qwen2ForCausalLM and Safetensors metadata reports 32,763,876,352 parameters. According to the model card, it belongs to the Qwen2.5 series with stronger coding, mathematics, instruction-following, and long-context behavior.
According to the model card, this is the instruction-tuned 32B Qwen2.5 model covering pretraining and post-training stages, within a series ranging from 0.5B to 72B parameters.
According to the model card, context reaches 131,072 tokens with up to 8,192 tokens of generation.
According to the model card, the model handles structured data such as tables and generates structured outputs including JSON, with 64 layers and grouped-query attention using 40 Q heads and 8 KV heads.
According to the model card, a chat-template snippet and a vLLM documentation reference are provided for deployment.
Source: Qwen/Qwen2.5-32B-Instruct
Captured: Unknown. Processed: 2026-09-07T19:34:35.999185+00:00.
Qwen2.5-32B-Instruct Introduction Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: Significantly more knowledge and has greatly improved capabilities in coding and mathematics , thanks to our specialized expert models in these domains. Significant improvements in instruction following , generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilien…
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