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README.md CHANGED
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Qwen3-30B-A3B
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+
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+ ## Qwen3 Highlights
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+
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+ Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models.
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+ Building upon extensive advancements in training data, model architecture, and optimization techniques, Qwen3 delivers the following key improvements over the previously released Qwen2.5:
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+
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+ - **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.
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+ - **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.
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+ - **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.
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+ - **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.
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+
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+ ## Model Overview
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+
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+ **Qwen3-30B-A3B** has the following features:
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+ - Type: Causal Language Models
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+ - Training Stage: Pretraining & Post-training
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+ - Number of Parameters: 30.5B in total and 3.3B activated
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+ - Number of Paramaters (Non-Embedding): 29.9B
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+ - Number of Layers: 48
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+ - Number of Attention Heads (GQA): 32 for Q and 4 for KV
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+ - Number of Experts: 128
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+ - Number of Activated Experts: 8
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+ - Context Length: 32,768
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+
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+ For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
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+
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+ ## Requirements
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+
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+ The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
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+
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+ With `transformers<4.51.0`, you will encounter the following error:
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+ ```
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+ KeyError: 'qwen3_moe'
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+ ```
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+
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+ ## Evaluation & Performance
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+
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+ Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen3/).
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+
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+ ### Citation
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+
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+ If you find our work helpful, feel free to give us a cite.
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+
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+ ```
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+ @misc{qwen3,
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+ title = {Qwen3},
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+ url = {https://qwenlm.github.io/blog/qwen3/},
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+ author = {Qwen Team},
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+ month = {April},
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+ year = {2025}
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+ }
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+ ```
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