Qwen3 is the latest generation of large language models in 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 techiques 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
Qwen3-8B-Base
has the following features:
Type: Causal Language Models
Training Stage: Pretraining
Number of Parameters: 8.2B
Number of Paramaters (Non-Embedding): 6.95B
Number of Layers: 36
Number of Attention Heads (GQA): 32 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
.
Requirements
The code of Qwen3 has been in the latest Hugging Face
transformers
and we advise you to use the latest version of
transformers
.
With
transformers<4.51.0
, you will encounter the following error:
KeyError: 'qwen3'
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}
}
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