unsloth/Qwen3-1.7B

Hugging Face
TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:2BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Apr 28, 2025Architecture:Transformer0.0K Featherless Exclusive Warm

Qwen3-1.7B is a 1.7 billion parameter causal language model from the Qwen series, developed by Qwen Team. It features a unique capability to seamlessly switch between a 'thinking mode' for complex reasoning tasks like math and coding, and a 'non-thinking mode' for efficient general-purpose dialogue. This model excels in reasoning, instruction-following, agent capabilities, and multilingual support across over 100 languages, making it suitable for diverse applications requiring adaptable intelligence.

Loading preview...

Qwen3-1.7B: Adaptable Intelligence with Thinking Modes

Qwen3-1.7B is a 1.7 billion parameter causal language model from the Qwen series, developed by the Qwen Team. It introduces a novel feature allowing seamless switching between two distinct operational modes:

Key Capabilities

  • Thinking Mode: Engages advanced reasoning for complex logical tasks, mathematics, and code generation, significantly enhancing performance in these areas.
  • Non-Thinking Mode: Optimized for efficient, general-purpose dialogue and instruction following, aligning with the functionality of previous Qwen2.5-Instruct models.
  • Superior Human Preference Alignment: Excels in creative writing, role-playing, and multi-turn conversations, delivering engaging and natural interactions.
  • Advanced Agent Capabilities: Integrates precisely with external tools, achieving leading performance in complex agent-based tasks among open-source models.
  • Multilingual Support: Supports over 100 languages and dialects with strong capabilities for multilingual instruction following and translation.

Good For

  • Applications requiring dynamic switching between analytical reasoning and conversational efficiency.
  • Complex problem-solving in mathematics and coding.
  • Creative content generation and interactive role-playing scenarios.
  • Developing sophisticated AI agents with tool-use capabilities.
  • Multilingual applications needing robust instruction following and translation.