Qwen3 0.6B Base

🚀 Qwen3-0.6B-Base

Qwen3-0.6B-Base is a powerful causal language model from the Qwen series, offering high - quality text generation capabilities with a large context length and advanced training techniques.

🚀 Quick Start

The code of Qwen3 has been included in the latest Hugging Face transformers. We recommend using the latest version of transformers.

If you use transformers<4.51.0, you will encounter the following error:

KeyError: 'qwen3'

✨ Features

Qwen3 Highlights

Qwen3 is the latest generation of large language models in the Qwen series, offering a comprehensive suite of dense and mixture - of - experts (MoE) models. Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following key improvements over the previously released Qwen2.5:

• Expanded Higher - Quality Pre - training Corpus: Qwen3 is pre - trained on 36 trillion tokens across 119 languages — tripling the language coverage of Qwen2.5 — with a much richer mix of high - quality data, including coding, STEM, reasoning, book, multilingual, and synthetic data.
• Training Techniques and Model Architecture: Qwen3 incorporates a series of training techniques and architectural refinements, including global - batch load balancing loss for MoE models and qk layernorm for all models, leading to improved stability and overall performance.
• Three - stage Pre - training: Stage 1 focuses on broad language modeling and general knowledge acquisition, Stage 2 improves reasoning skills like STEM, coding, and logical reasoning, and Stage 3 enhances long - context comprehension by extending training sequence lengths up to 32k tokens.
• Scaling Law Guided Hyperparameter Tuning: Through comprehensive scaling law studies across the three - stage pre - training pipeline, Qwen3 systematically tunes critical hyperparameters — such as learning rate scheduler and batch size — separately for dense and MoE models, resulting in better training dynamics and final performance across different model scales.

Model Overview

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

📚 Documentation

Evaluation & Performance

Detailed evaluation results are reported in this 📑 blog.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen3,
    title  = {Qwen3},
    url    = {https://qwenlm.github.io/blog/qwen3/},
    author = {Qwen Team},
    month  = {April},
    year   = {2025}
}

📄 License

This project is licensed under the Apache - 2.0 license.