Qwen3 4B Thinking 2507 Highlights Over the past three months, we have continued to scale the thinking capability of Qwen3 4B, improving both the quality and depth of reasoning. We are pleased to introduce Qwen3 4B Thinking 2507 , featuring the following key enhancements: Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise. Markedly better general capabilities , such as instruction following, tool usage, text generation, and alignment with human preferences. Enhanced 256K long context understanding capabilities. NOTE : This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks. Model Overview Qwen3 4B Thinking 2507 has the following features: Type: Causal Language Models Training Stage: Pretraining & Post training Number of Parameters: 4.0B Number of Paramaters (Non Embedding): 3.6B Number of Layers: 36 Number of Attention Heads (GQA): 32 for Q and 8 for KV Context Length: 262,144 natively . NOTE: This model supports only thinking mode. Meanwhile, specifying enable thinking=True is no longer required. Additio…
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