Add languages tag

README.md

@@ -1,59 +1,178 @@
----
-tags:
-- unsloth
-base_model:
-- Qwen/Qwen3-30B-A3B-Base
----
-# Qwen3-30B-A3B
-
-## Qwen3 Highlights
-
-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
-
-**Qwen3-30B-A3B** has the following features:
-- Type: Causal Language Models
-- Training Stage: Pretraining & Post-training
-- Number of Parameters: 30.5B in total and 3.3B activated
-- Number of Paramaters (Non-Embedding): 29.9B
-- Number of Layers: 48
-- Number of Attention Heads (GQA): 32 for Q and 4 for KV
-- Number of Experts: 128
-- Number of Activated Experts: 8
-- Context Length: 32,768
-
-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/).
-
-## Requirements
-
-The code of Qwen3-MoE 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_moe'
-```
-
-## Evaluation & Performance
-
-Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen3/).
-
-### 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}
-}
+---
+tags:
+- unsloth
+base_model:
+- Qwen/Qwen3-30B-A3B-Base
+language:
+- eng
+- fra
+- por
+- deu
+- ron
+- swe
+- dan
+- bul
+- rus
+- ces
+- ell
+- ukr
+- spa
+- nld
+- slk
+- hrv
+- pol
+- lit
+- nob
+- nno
+- fas
+- slv
+- guj
+- lav
+- ita
+- oci
+- nep
+- mar
+- bel
+- srp
+- ltz
+- vec
+- asm
+- cym
+- szl
+- ast
+- hne
+- awa
+- mai
+- bho
+- snd
+- gle
+- fao
+- hin
+- pan
+- ben
+- ori
+- tgk
+- ydd
+- lmo
+- lij
+- scn
+- fur
+- srd
+- glg
+- cat
+- isl
+- als
+- lim
+- prs
+- afr
+- mkd
+- sin
+- urd
+- mag
+- bos
+- hye
+- zho
+- yue
+- mya
+- ara
+- ars
+- apc
+- arz
+- ary
+- acm
+- acq
+- aeb
+- heb
+- mlt
+- ind
+- zsm
+- tgl
+- ceb
+- jav
+- sun
+- min
+- ban
+- bjn
+- pag
+- ilo
+- war
+- tam
+- tel
+- kan
+- mal
+- tur
+- azj
+- uzn
+- kaz
+- bak
+- tat
+- tha
+- lao
+- fin
+- est
+- hun
+- vie
+- khm
+- jpn
+- kor
+- kat
+- eus
+- hat
+- pap
+- kea
+- tpi
+- swa
+---
+# Qwen3-30B-A3B
+
+## Qwen3 Highlights
+
+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
+
+**Qwen3-30B-A3B** has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training
+- Number of Parameters: 30.5B in total and 3.3B activated
+- Number of Paramaters (Non-Embedding): 29.9B
+- Number of Layers: 48
+- Number of Attention Heads (GQA): 32 for Q and 4 for KV
+- Number of Experts: 128
+- Number of Activated Experts: 8
+- Context Length: 32,768
+
+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/).
+
+## Requirements
+
+The code of Qwen3-MoE 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_moe'
+```
+
+## Evaluation & Performance
+
+Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen3/).
+
+### 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}
+}
 ```