Improve language tag

README.md

@@ -1,77 +1,91 @@
----
-base_model:
-- Qwen/Qwen2.5-Coder-1.5B-Instruct
-- Qwen/Qwen2.5-Math-1.5B-Instruct
-- Qwen/Qwen2.5-1.5B
-- agentica-org/DeepScaleR-1.5B-Preview
-tags:
-- merge
-- mergekit
-- lazymergekit
-- Qwen/Qwen2.5-Coder-1.5B-Instruct
-- Qwen/Qwen2.5-Math-1.5B-Instruct
-- Qwen/Qwen2.5-1.5B
-- agentica-org/DeepScaleR-1.5B-Preview
----
-
-# DeepQwenScalerPlus
-
-DeepQwenScalerPlus is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
-* [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
-* [Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct)
-* [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B)
-* [agentica-org/DeepScaleR-1.5B-Preview](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview)
-
-## 🧩 Configuration
-
-```yaml
-models:
-  - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
-    # no parameters necessary for base model
-  - model: Qwen/Qwen2.5-Coder-1.5B-Instruct
-    parameters:
-      density: 0.5
-      weight: 0.5
-  - model: Qwen/Qwen2.5-Math-1.5B-Instruct
-    parameters:
-      density: 0.6
-      weight: 0.5
-  - model: Qwen/Qwen2.5-1.5B
-    parameters:
-      density: 0.6
-      weight: 0.5
-  - model: agentica-org/DeepScaleR-1.5B-Preview
-    parameters:
-      density: 0.4
-      weight: 0.6
-merge_method: ties
-base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
-parameters:
-  normalize: true
-dtype: float16
-```
-
-## 💻 Usage
-
-```python
-!pip install -qU transformers accelerate
-
-from transformers import AutoTokenizer
-import transformers
-import torch
-
-model = "K00B404/DeepQwenScalerPlus"
-messages = [{"role": "user", "content": "What is a large language model?"}]
-
-tokenizer = AutoTokenizer.from_pretrained(model)
-prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-pipeline = transformers.pipeline(
-    "text-generation",
-    model=model,
-    torch_dtype=torch.float16,
-    device_map="auto",
-)
-
-outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
-print(outputs[0]["generated_text"])
+---
+base_model:
+- Qwen/Qwen2.5-Coder-1.5B-Instruct
+- Qwen/Qwen2.5-Math-1.5B-Instruct
+- Qwen/Qwen2.5-1.5B
+- agentica-org/DeepScaleR-1.5B-Preview
+tags:
+- merge
+- mergekit
+- lazymergekit
+- Qwen/Qwen2.5-Coder-1.5B-Instruct
+- Qwen/Qwen2.5-Math-1.5B-Instruct
+- Qwen/Qwen2.5-1.5B
+- agentica-org/DeepScaleR-1.5B-Preview
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+# DeepQwenScalerPlus
+
+DeepQwenScalerPlus is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
+* [Qwen/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B-Instruct)
+* [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B)
+* [agentica-org/DeepScaleR-1.5B-Preview](https://huggingface.co/agentica-org/DeepScaleR-1.5B-Preview)
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+    # no parameters necessary for base model
+  - model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+    parameters:
+      density: 0.5
+      weight: 0.5
+  - model: Qwen/Qwen2.5-Math-1.5B-Instruct
+    parameters:
+      density: 0.6
+      weight: 0.5
+  - model: Qwen/Qwen2.5-1.5B
+    parameters:
+      density: 0.6
+      weight: 0.5
+  - model: agentica-org/DeepScaleR-1.5B-Preview
+    parameters:
+      density: 0.4
+      weight: 0.6
+merge_method: ties
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+parameters:
+  normalize: true
+dtype: float16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "K00B404/DeepQwenScalerPlus"
+messages = [{"role": "user", "content": "What is a large language model?"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
 ```