Improve language tag

README.md

@@ -1,74 +1,88 @@
----
-license: mit
-base_model:
-- PRIME-RL/Eurus-2-7B-PRIME
-- Qwen/Qwen2.5-7B-Instruct
-tags:
-- merge
-- mergekit
-- lazymergekit
----
-
-# Qwerus-7B
-
-Qwerus-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
-* [PRIME-RL/Eurus-2-7B-PRIME](https://huggingface.co/PRIME-RL/Eurus-2-7B-PRIME)
-* [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
-
-Benchmark on reasoning tasks using lighteval:
-
-|      Task       |Version|     Metric     |Value |   |Stderr|
-|-----------------|------:|----------------|-----:|---|-----:|
-|aime24  |      1|extractive_match|0.1333|±  |0.0631|
-|math_500|      1|extractive_match|0.7420|±  |0.0196|
-
-In comparison, Qwen2.5-7B-Instruct:
-
-|      Task       |Version|     Metric     |Value |   |Stderr|
-|-----------------|------:|----------------|-----:|---|-----:|
-|aime24  |      1|extractive_match|0.1667|±  |0.0692|
-|math_500|      1|extractive_match|0.8220|±  |0.0171|
-
-## 🧩 Configuration
-
-```yaml
-models:
-  - model: Qwen/Qwen2.5-7B
-    # No parameters necessary for base model
-  - model: PRIME-RL/Eurus-2-7B-PRIME
-    parameters:
-      density: 0.56
-      weight: 0.5
-  - model: Qwen/Qwen2.5-7B-Instruct
-    parameters:
-      density: 0.56
-      weight: 0.5
-merge_method: dare_ties
-base_model: Qwen/Qwen2.5-7B
-dtype: bfloat16
-```
-
-## 💻 Usage
-
-```python
-!pip install -qU transformers accelerate
-
-from transformers import AutoTokenizer
-import transformers
-import torch
-
-model = "mlabonne/Qwerus-7B"
-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"])
+---
+license: mit
+base_model:
+- PRIME-RL/Eurus-2-7B-PRIME
+- Qwen/Qwen2.5-7B-Instruct
+tags:
+- merge
+- mergekit
+- lazymergekit
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+
+# Qwerus-7B
+
+Qwerus-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [PRIME-RL/Eurus-2-7B-PRIME](https://huggingface.co/PRIME-RL/Eurus-2-7B-PRIME)
+* [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+
+Benchmark on reasoning tasks using lighteval:
+
+|      Task       |Version|     Metric     |Value |   |Stderr|
+|-----------------|------:|----------------|-----:|---|-----:|
+|aime24  |      1|extractive_match|0.1333|±  |0.0631|
+|math_500|      1|extractive_match|0.7420|±  |0.0196|
+
+In comparison, Qwen2.5-7B-Instruct:
+
+|      Task       |Version|     Metric     |Value |   |Stderr|
+|-----------------|------:|----------------|-----:|---|-----:|
+|aime24  |      1|extractive_match|0.1667|±  |0.0692|
+|math_500|      1|extractive_match|0.8220|±  |0.0171|
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: Qwen/Qwen2.5-7B
+    # No parameters necessary for base model
+  - model: PRIME-RL/Eurus-2-7B-PRIME
+    parameters:
+      density: 0.56
+      weight: 0.5
+  - model: Qwen/Qwen2.5-7B-Instruct
+    parameters:
+      density: 0.56
+      weight: 0.5
+merge_method: dare_ties
+base_model: Qwen/Qwen2.5-7B
+dtype: bfloat16
+```
+
+## 💻 Usage
+
+```python
+!pip install -qU transformers accelerate
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "mlabonne/Qwerus-7B"
+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"])
 ```