QwenLM/Qwen3

QwenLM/Qwen3

Description: Qwen3 is the large language model series developed by Qwen team, Alibaba Cloud.

Language: Python

Stars: 27302

Forks: 1993

Open issues: 53

Created: 2024-02-05T05:45:08Z

Pushed: 2026-01-09T03:05:47Z

Default branch: main

Fork: no

Archived: no

README:

Qwen3

💜 Qwen Chat&nbsp&nbsp | &nbsp&nbsp🤗 Hugging Face&nbsp&nbsp | &nbsp&nbsp🤖 ModelScope&nbsp&nbsp | &nbsp&nbsp 📑 Paper &nbsp&nbsp | &nbsp&nbsp 📑 Blog &nbsp&nbsp ｜ &nbsp&nbsp📖 Documentation

🖥️ Demo&nbsp&nbsp | &nbsp&nbsp💬 WeChat (微信)&nbsp&nbsp | &nbsp&nbsp🫨 Discord&nbsp&nbsp

Visit our Hugging Face or ModelScope organization (click links above), search checkpoints with names starting with Qwen3- or visit the Qwen3 collection, and you will find all you need! Enjoy!

To learn more about Qwen3, feel free to read our documentation \[EN|ZH\]. Our documentation consists of the following sections:

• Quickstart: the basic usages and demonstrations;
• Inference: the guidance for the inference with Transformers, including batch inference, streaming, etc.;
• Run Locally: the instructions for running LLM locally on CPU and GPU, with frameworks like llama.cpp, Ollama, and LM Studio;
• Deployment: the demonstration of how to deploy Qwen for large-scale inference with frameworks like SGLang, vLLM, TGI, etc.;
• Quantization: the practice of quantizing LLMs with GPTQ, AWQ, as well as the guidance for how to make high-quality quantized GGUF files;
• Training: the instructions for post-training, including SFT and RLHF (TODO) with frameworks like Axolotl, LLaMA-Factory, etc.
• Framework: the usage of Qwen with frameworks for application, e.g., RAG, Agent, etc.

Introduction

Qwen3-2507

Over the past three months, we continued to explore the potential of the Qwen3 families and we are excited to introduce the updated Qwen3-2507 in two variants, Qwen3-Instruct-2507 and Qwen3-Thinking-2507, and three sizes, 235B-A22B, 30B-A3B, and 4B.

Qwen3-Instruct-2507 is the updated version of the previous Qwen3 non-thinking mode, featuring the following key enhancements:

• Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
• Substantial gains in long-tail knowledge coverage across multiple languages.
• Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
• Enhanced capabilities in 256K-token long-context understanding, extendable up to 1 million tokens.

Qwen3-Thinking-2507 is the continuation of Qwen3 thinking model, with improved quality and depth of reasoning, featuring the following key enhancements:

• Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise — achieving state-of-the-art results among open-weight thinking models.
• Markedly better general capabilities, such as instruction following, tool usage, text generation, and alignment with human preferences.
• Enhanced 256K long-context understanding capabilities, extendable up to 1 million tokens.

Previous Qwen3 Release Qwen3 (aka Qwen3-2504)

We are excited to announce the release of Qwen3, the latest addition to the Qwen family of large language models. These models represent our most advanced and intelligent systems to date, improving from our experience in building QwQ and Qwen2.5. We are making the weights of Qwen3 available to the public, including both dense and Mixture-of-Expert (MoE) models.

The highlights from Qwen3 include:

Dense and Mixture-of-Experts (MoE) models of various sizes, available in 0.6B, 1.7B, 4B, 8B, 14B, 32B and 30B-A3B, 235B-A22B.

Seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose chat), ensuring optimal performance across various scenarios.

Significantly enhancement in 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.

News

• 2025.08.08: You can now use Qwen3-2507 to handle ultra-long inputs of 1 million tokens! See the update modelcards (235B-A22B-Instruct-2507, 235B-A22B-Thinking-2507, A30B-A3B-Instruct-2507, A30B-A3B-Thinking-2507) for how to enable this feature.
• 2025.08.06: The final open release of Qwen3-2507, Qwen3-4B-Instruct-2507 and Qwen3-4B-Thinking-2507, is out!
• 2025.07.31: Qwen3-30B-A3B-Thinking-2507 is released. Check out the modelcard for more details!
• 2025.07.30: Qwen3-30B-A3B-Instruct-2507 is released. Check out the modelcard for more details!
• 2025.07.25: We released the updated version of Qwen3-235B-A22B thinking mode, named Qwen3-235B-A22B-Thinking-2507. Check out the modelcard for more details!
• 2025.07.21: We released the updated version of Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507, featuring significant enhancements over the previous version and supporting 256K-token long-context understanding. Check our…