Qwen3-30B-A3B-Instruct-2507 – a non-thinking mode model open-sourced by Alibaba Tongyi

What is Qwen3-30B-A3B-Instruct-2507

Qwen3-30B-A3B-Instruct-2507 is an open-source non-thinking mode language model by Alibaba Tongyi, with a total of 30.5 billion parameters and 3.3 billion active parameters. It has 48 layers and supports a context length of 262,144 tokens. The model excels in instruction following, logical reasoning, and multilingual knowledge coverage, making it especially suitable for local deployment with relatively low hardware requirements. It can be efficiently deployed using sglang or vllm and is a powerful tool for developers and researchers. It is currently accessible through Qwen Chat.

Key Features of Qwen3-30B-A3B-Instruct-2507

• Instruction Following
  Accurately understands and executes user instructions to generate compliant text outputs.

• Logical Reasoning
  Strong capability in logical reasoning, handling complex logic problems and inference tasks.

• Text Understanding and Generation
  Understands and produces high-quality text suitable for various NLP tasks such as writing, translation, and Q&A.

• Mathematical and Scientific Problem Solving
  Excels in solving complex math and science problems with advanced calculation and reasoning.

• Coding Ability
  Supports code generation and programming-related tasks, helping developers quickly fulfill coding needs.

• Multilingual Support
  Covers multiple languages with strong cross-lingual understanding and generation capabilities.

• Long-Text Processing
  Supports a context window of up to 262,144 tokens, suitable for long input and generation tasks.

• Tool Invocation
  Based on Qwen-Agent, supports calling external tools to enhance practical usability.

Technical Principles of Qwen3-30B-A3B-Instruct-2507

• Mixture of Experts (MoE)
  With 30.5 billion total parameters and 3.3 billion active parameters, the model uses a sparse activation mechanism. It contains 128 experts, activating 8 experts per input dynamically, which improves efficiency and flexibility while maintaining performance.

• Causal Language Model
  Built on a Transformer architecture with 48 layers, each with 32 query heads and 4 key-value heads. It handles long sequences effectively with support for a 262,144-token context length, suited for scenarios requiring long-context understanding.

• Pretraining
  Trained on large-scale text data to learn general language features and patterns.

• Fine-tuning
  Further fine-tuned on task-specific datasets to boost performance on specialized tasks.

Project Resources

• HuggingFace Model Hub: https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507

Application Scenarios of Qwen3-30B-A3B-Instruct-2507

• Writing Assistance
  Helps writers and content creators rapidly generate high-quality text, improving writing efficiency.

• Intelligent Customer Service
  Builds smart customer support systems to automatically answer queries, enhancing satisfaction and response speed.

• Programming Assistance
  Generates code snippets, optimization suggestions, and API documentation for developers to improve productivity and code quality.

• Educational Tutoring
  Provides answers to academic questions and learning support for students, assisting teachers in generating teaching materials and exercises.

• Multilingual Translation
  Supports translation across multiple languages, facilitating cross-language communication and international content generation.