Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

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README.md +208 -196

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@@ -1,196 +1,208 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- LCoT
-- Qwen
-- v2
-datasets:
-- PowerInfer/QWQ-LONGCOT-500K
-- AI-MO/NuminaMath-CoT
-- prithivMLmods/Math-Solve
-- amphora/QwQ-LongCoT-130K
-- prithivMLmods/Deepthink-Reasoning
-model-index:
-- name: QwQ-LCoT2-7B-Instruct
-  results:
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: IFEval (0-Shot)
-      type: wis-k/instruction-following-eval
-      split: train
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: inst_level_strict_acc and prompt_level_strict_acc
-      value: 55.76
-      name: averaged accuracy
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: BBH (3-Shot)
-      type: SaylorTwift/bbh
-      split: test
-      args:
-        num_few_shot: 3
-    metrics:
-    - type: acc_norm
-      value: 34.37
-      name: normalized accuracy
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MATH Lvl 5 (4-Shot)
-      type: lighteval/MATH-Hard
-      split: test
-      args:
-        num_few_shot: 4
-    metrics:
-    - type: exact_match
-      value: 22.21
-      name: exact match
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: GPQA (0-shot)
-      type: Idavidrein/gpqa
-      split: train
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: acc_norm
-      value: 6.38
-      name: acc_norm
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MuSR (0-shot)
-      type: TAUR-Lab/MuSR
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: acc_norm
-      value: 15.75
-      name: acc_norm
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MMLU-PRO (5-shot)
-      type: TIGER-Lab/MMLU-Pro
-      config: main
-      split: test
-      args:
-        num_few_shot: 5
-    metrics:
-    - type: acc
-      value: 37.13
-      name: accuracy
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
-      name: Open LLM Leaderboard
----
-# **QwQ-LCoT2-7B-Instruct**
-The *QwQ-LCoT2-7B-Instruct* is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5-7B base model and has been fine-tuned on the chain of thought reasoning datasets, focusing on chain-of-thought (CoT) reasoning for problems. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
-# **Quickstart with Transformers**
-Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "prithivMLmods/QwQ-LCoT2-7B-Instruct"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-prompt = "How many r in strawberry."
-messages = [
-    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=512
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
-# **Intended Use**
-The QwQ-LCoT2-7B-Instruct model is designed for advanced reasoning and instruction-following tasks, with specific applications including:
-1. **Instruction Following**: Providing detailed and step-by-step guidance for a wide range of user queries.
-2. **Logical Reasoning**: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios.
-3. **Text Generation**: Crafting coherent, contextually relevant, and well-structured text in response to prompts.
-4. **Problem-Solving**: Analyzing and addressing tasks that require chain-of-thought (CoT) reasoning, making it ideal for education, tutoring, and technical support.
-5. **Knowledge Enhancement**: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.
-# **Limitations**
-1. **Data Bias**: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data.
-2. **Context Limitation**: Performance may degrade for tasks requiring knowledge or reasoning that significantly exceeds the model's pretraining or fine-tuning context.
-3. **Complexity Ceiling**: While optimized for multi-step reasoning, exceedingly complex or abstract problems may result in incomplete or incorrect outputs.
-4. **Dependency on Prompt Quality**: The quality and specificity of the user prompt heavily influence the model's responses.
-5. **Non-Factual Outputs**: Despite being fine-tuned for reasoning, the model can still generate hallucinated or factually inaccurate content, particularly for niche or unverified topics.
-6. **Computational Requirements**: Running the model effectively requires significant computational resources, particularly when generating long sequences or handling high-concurrency workloads.
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__QwQ-LCoT2-7B-Instruct-details)!
-Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FQwQ-LCoT2-7B-Instruct&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
-|      Metric       |Value (%)|
-|-------------------|--------:|
-|**Average**        |    28.60|
-|IFEval (0-Shot)    |    55.76|
-|BBH (3-Shot)       |    34.37|
-|MATH Lvl 5 (4-Shot)|    22.21|
-|GPQA (0-shot)      |     6.38|
-|MuSR (0-shot)      |    15.75|
-|MMLU-PRO (5-shot)  |    37.13|

+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- LCoT
+- Qwen
+- v2
+datasets:
+- PowerInfer/QWQ-LONGCOT-500K
+- AI-MO/NuminaMath-CoT
+- prithivMLmods/Math-Solve
+- amphora/QwQ-LongCoT-130K
+- prithivMLmods/Deepthink-Reasoning
+model-index:
+- name: QwQ-LCoT2-7B-Instruct
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: wis-k/instruction-following-eval
+      split: train
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 55.76
+      name: averaged accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: SaylorTwift/bbh
+      split: test
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 34.37
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: lighteval/MATH-Hard
+      split: test
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 22.21
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      split: train
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 6.38
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 15.75
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 37.13
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT2-7B-Instruct
+      name: Open LLM Leaderboard
+---
+# **QwQ-LCoT2-7B-Instruct**
+The *QwQ-LCoT2-7B-Instruct* is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5-7B base model and has been fine-tuned on the chain of thought reasoning datasets, focusing on chain-of-thought (CoT) reasoning for problems. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
+# **Quickstart with Transformers**
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "prithivMLmods/QwQ-LCoT2-7B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "How many r in strawberry."
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+# **Intended Use**
+The QwQ-LCoT2-7B-Instruct model is designed for advanced reasoning and instruction-following tasks, with specific applications including:
+1. **Instruction Following**: Providing detailed and step-by-step guidance for a wide range of user queries.
+2. **Logical Reasoning**: Solving problems requiring multi-step thought processes, such as math problems or complex logic-based scenarios.
+3. **Text Generation**: Crafting coherent, contextually relevant, and well-structured text in response to prompts.
+4. **Problem-Solving**: Analyzing and addressing tasks that require chain-of-thought (CoT) reasoning, making it ideal for education, tutoring, and technical support.
+5. **Knowledge Enhancement**: Leveraging reasoning datasets to offer deeper insights and explanations for a wide variety of topics.
+# **Limitations**
+1. **Data Bias**: As the model is fine-tuned on specific datasets, its outputs may reflect inherent biases from the training data.
+2. **Context Limitation**: Performance may degrade for tasks requiring knowledge or reasoning that significantly exceeds the model's pretraining or fine-tuning context.
+3. **Complexity Ceiling**: While optimized for multi-step reasoning, exceedingly complex or abstract problems may result in incomplete or incorrect outputs.
+4. **Dependency on Prompt Quality**: The quality and specificity of the user prompt heavily influence the model's responses.
+5. **Non-Factual Outputs**: Despite being fine-tuned for reasoning, the model can still generate hallucinated or factually inaccurate content, particularly for niche or unverified topics.
+6. **Computational Requirements**: Running the model effectively requires significant computational resources, particularly when generating long sequences or handling high-concurrency workloads.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__QwQ-LCoT2-7B-Instruct-details)!
+Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FQwQ-LCoT2-7B-Instruct&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
+|      Metric       |Value (%)|
+|-------------------|--------:|
+|**Average**        |    28.60|
+|IFEval (0-Shot)    |    55.76|
+|BBH (3-Shot)       |    34.37|
+|MATH Lvl 5 (4-Shot)|    22.21|
+|GPQA (0-shot)      |     6.38|
+|MuSR (0-shot)      |    15.75|
+|MMLU-PRO (5-shot)  |    37.13|