ggbetz/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:Mar 1, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Loading

Qwen3-1.7B is a 1.7 billion parameter causal language model from the Qwen series, developed by Qwen. 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 dialogue. This model excels in reasoning, instruction-following, agent capabilities, and multilingual support across 100+ languages, making it suitable for diverse conversational and analytical applications.

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Overview

Qwen3-1.7B is a 1.7 billion parameter causal language model from the Qwen series, developed by Qwen. It is distinguished by its innovative ability to operate in two distinct modes: a 'thinking mode' for complex logical reasoning, mathematics, and code generation, and a 'non-thinking mode' for efficient, general-purpose dialogue. This flexibility allows for optimized performance across a wide range of scenarios.

Key Capabilities

  • Dual-Mode Operation: Seamlessly switches between a reasoning-focused 'thinking mode' and an efficient 'non-thinking mode' within a single model.
  • Enhanced Reasoning: Demonstrates significant improvements in mathematical problem-solving, code generation, and commonsense logical reasoning.
  • Human Preference Alignment: Excels in creative writing, role-playing, multi-turn conversations, and instruction following, providing a natural conversational experience.
  • Agentic Capabilities: Integrates precisely with external tools in both thinking and non-thinking modes, achieving strong performance in complex agent-based tasks.
  • Multilingual Support: Supports over 100 languages and dialects with robust multilingual instruction following and translation abilities.

Usage Notes

  • Sampling Parameters: Specific Temperature, TopP, TopK, and MinP settings are recommended for optimal performance in both thinking and non-thinking modes.
  • Output Length: An output length of 32,768 tokens is recommended for most queries, with up to 38,912 tokens for highly complex problems.
  • Agent Integration: Recommended for use with Qwen-Agent for streamlined tool-calling and agentic workflows.

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