Add languages tag
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README.md
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---
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tags:
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- unsloth
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base_model:
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- Qwen/Qwen3-4B-Base
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license: apache-2.0
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```
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---
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tags:
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| 3 |
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- unsloth
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base_model:
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- Qwen/Qwen3-4B-Base
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license: apache-2.0
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language:
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- eng
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- fra
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- por
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- deu
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- ron
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- swe
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- dan
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- bul
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- rus
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- ces
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- ell
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- ukr
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- spa
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- nld
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- slk
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- hrv
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- pol
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- lit
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- nob
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- nno
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- fas
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- slv
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- guj
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- lav
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- ita
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- oci
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- nep
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- mar
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- bel
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- srp
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- ltz
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- vec
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- asm
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- cym
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- szl
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- ast
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- hne
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- awa
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- mai
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- bho
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- snd
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- gle
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- fao
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- hin
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- pan
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- ben
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- ori
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- tgk
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- ydd
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- lmo
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- lij
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- scn
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- fur
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- srd
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- glg
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- cat
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- isl
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- als
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- lim
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- prs
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- afr
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- mkd
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- sin
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- urd
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- mag
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- bos
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- hye
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- zho
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- yue
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- mya
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- ara
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- ars
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- apc
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- arz
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- ary
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- acm
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- acq
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- aeb
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- heb
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- mlt
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- ind
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- zsm
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- tgl
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- ceb
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- jav
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- sun
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- min
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- ban
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- bjn
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- pag
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- ilo
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- war
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- tam
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- tel
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- kan
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- mal
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- tur
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- azj
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- uzn
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- kaz
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- bak
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- tat
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- tha
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- lao
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- fin
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- est
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- hun
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- vie
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- khm
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- jpn
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- kor
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- kat
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- eus
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- hat
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- pap
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- kea
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- tpi
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- swa
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---
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# Qwen3-4B-Base
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## Qwen3 Highlights
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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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- **Expanded Higher-Quality Pre-training Corpus:** Qwen3 is pre-trained on 36 trillion tokens across 119 languages β tripling the language coverage of Qwen2.5 β 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 β such as learning rate scheduler and batch size β separately for dense and MoE models, resulting in better training dynamics and final performance across different model scales.
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## Model Overview
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**Qwen3-4B-Base** has the following features:
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- Type: Causal Language Models
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- Training Stage: Pretraining
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- Number of Parameters: 4.0B
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- Number of Paramaters (Non-Embedding): 3.6B
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- Number of Layers: 36
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- Number of Attention Heads (GQA): 32 for Q and 8 for KV
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- Context Length: 32,768
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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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## Requirements
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The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
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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'
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```
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## Evaluation & Performance
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Detailed evaluation results are reported in this [π blog](https://qwenlm.github.io/blog/qwen3/).
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### Citation
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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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@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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