Qwen3 Highlights

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:

• Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios.
• Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
• Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
• Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
• Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation.

Model Overview

Qwen3-0.6B has the following features:

• Type: Causal Language Models
• Training Stage: Pretraining & Post-training
• Number of Parameters: 0.6B
• Number of Paramaters (Non-Embedding): 0.44B
• Number of Layers: 28
• Number of Attention Heads (GQA): 16 for Q and 8 for KV
• Context Length: 32,768

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Quickstart

• vLLM:

For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.

Switching Between Thinking and Non-Thinking Mode

INLINECODE231020A51F

We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.

CODEBLOCK338E93D31

In this mode, the model will not generate any think content and will not include a INLINECODE247D1A75A6 block.

> [!NOTE]
> For non-thinking mode, we suggest using INLINECODE25EFF73B13, INLINECODE263F391C47, INLINECODE275112A2D5, and INLINECODE285F15EA12. For more detailed guidance, please refer to the Best Practices section.

Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input

Here is an example of a multi-turn conversation:

Agentic Use

Qwen3 excels in tool calling capabilities. We recommend using Qwen-Agent to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.

To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
INLINECODE42A8EA1C95INLINECODE434C58D377enablethinking=TrueINLINECODE44CF502B51Temperature=0.6INLINECODE451DF0DAA2TopP=0.95INLINECODE4695420AA3TopK=20INLINECODE47A17D23DFMinP=0INLINECODE4846B2A296enablethinking=FalseINLINECODE49C49D018ATemperature=0.7INLINECODE50107A165ATopP=0.8INLINECODE51D49306DFTopK=20INLINECODE52569ED95EMinP=0INLINECODE53782E8BC1presencepenaltyINLINECODE546B3B0CE3answerINLINECODE559712949D"answer": "C"`."

CODEBLOCK5_8E2CC9FB