motobrew/Qwen3-4B-Instruct-2507
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Mar 16, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

Qwen3-4B-Instruct-2507 is a 4.0 billion parameter causal language model developed by Qwen, featuring a native context length of 262,144 tokens. This updated version of the Qwen3-4B non-thinking mode demonstrates significant improvements in instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage. It excels in long-tail knowledge coverage across multiple languages and shows markedly better alignment with user preferences in subjective and open-ended tasks, making it suitable for generating high-quality, helpful responses.

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Qwen3-4B-Instruct-2507: Enhanced 4B Instruction-Tuned Model

Qwen3-4B-Instruct-2507 is an updated version of the Qwen3-4B non-thinking mode, developed by Qwen. This 4.0 billion parameter causal language model is designed for robust instruction following and features an impressive native context length of 262,144 tokens, with enhanced capabilities for 256K long-context understanding. It specifically operates in a non-thinking mode, simplifying deployment by not generating <think></think> blocks.

Key Capabilities

  • General Performance: Significant improvements across instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage.
  • Long-Tail Knowledge: Substantial gains in covering long-tail knowledge across multiple languages.
  • User Alignment: Markedly better alignment with user preferences for subjective and open-ended tasks, leading to more helpful and higher-quality text generation.
  • Agentic Use: Excels in tool calling capabilities, with recommendations to use Qwen-Agent for optimal agentic ability.

Good for

  • Applications requiring strong instruction following and logical reasoning in a compact model.
  • Tasks benefiting from extensive context understanding, up to 256K tokens.
  • Generating high-quality, aligned responses for subjective and open-ended prompts.
  • Developing agentic applications that leverage tool-calling functionalities.
  • Multilingual applications needing broad knowledge coverage.