Improve language tag

README.md

@@ -1,224 +1,230 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-14B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- opus
-- elite
-- calcium
-- trl
-- qwen
-model-index:
-- name: Calcium-Opus-14B-Elite2
-  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: 61.76
-      name: averaged accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
-      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: 46.81
-      name: normalized accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
-      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: 36.1
-      name: exact match
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
-      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: 16
-      name: acc_norm
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
-      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: 22.24
-      name: acc_norm
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
-      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: 47.79
-      name: accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
-      name: Open LLM Leaderboard
----
-
-![e2.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CbSQZghlvWbMo2EdMacXp.gif)
-
-# **Calcium-Opus-14B-Elite2**
-
-Calcium-Opus-14B-Elite2 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. These models have proven effective in context understanding, reasoning, and mathematical problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets, with a focus on chain-of-thought (CoT) reasoning for problem-solving. 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.
-
-Key improvements include:  
-1. **Enhanced Knowledge and Expertise**: The model demonstrates significantly more knowledge and greatly improved capabilities in coding and mathematics, thanks to specialized expert models in these domains.  
-2. **Improved Instruction Following**: It shows significant advancements in following instructions, generating long texts (over 8K tokens), understanding structured data (e.g., tables), and producing structured outputs, especially in JSON format.  
-3. **Better Adaptability**: The model is more resilient to diverse system prompts, enabling enhanced role-playing implementations and condition-setting for chatbots.  
-4. **Long-Context Support**: It offers long-context support of up to 128K tokens and can generate up to 8K tokens in a single output.  
-5. **Multilingual Proficiency**: The model supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
-
-# **Quickstart with transformers**
-
-Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate content:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/Calcium-Opus-14B-Elite2"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Give me a short introduction to large language model."
-messages = [
-    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
-    {"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**  
-1. **Reasoning and Context Understanding**:  
-   Designed to assist with complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction and critical thinking.  
-
-2. **Mathematical Problem-Solving**:  
-   Specialized for performing advanced mathematical reasoning and calculations, making it suitable for educational, scientific, and research-oriented applications.  
-
-3. **Code Generation and Debugging**:  
-   Offers robust support for coding tasks, including writing, debugging, and optimizing code in various programming languages, ideal for developers and software engineers.  
-
-4. **Structured Data Analysis**:  
-   Excels in processing and analyzing structured data, such as tables and JSON, and generating structured outputs, which is useful for data analysts and automation workflows.  
-
-5. **Multilingual Applications**:  
-   Supports over 29 languages, making it versatile for global applications like multilingual chatbots, content generation, and translations.  
-
-6. **Extended Content Generation**:  
-   Capable of generating long-form content (over 8K tokens), useful for writing reports, articles, and creating detailed instructional guides.  
-
-# **Limitations**  
-1. **Hardware Requirements**:  
-   Due to its 20B parameter size and support for long-context inputs, running the model requires significant computational resources, including high-memory GPUs or TPUs.  
-
-2. **Potential Bias in Multilingual Outputs**:  
-   While it supports 29 languages, the quality and accuracy of outputs may vary depending on the language, especially for less-resourced languages.  
-
-3. **Inconsistent Outputs for Creative Tasks**:  
-   The model may occasionally produce inconsistent or repetitive results in creative writing, storytelling, or highly subjective tasks.  
-
-4. **Limited Real-World Awareness**:  
-   It lacks real-time knowledge of current events beyond its training cutoff, which may limit its ability to respond accurately to the latest information.  
-
-5. **Error Propagation in Long-Text Outputs**:  
-   In generating long texts, minor errors in early outputs can sometimes propagate, reducing the overall coherence and accuracy of the response.  
-
-6. **Dependency on High-Quality Prompts**:  
-   Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.  
-
-# [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__Calcium-Opus-14B-Elite2-details)!
-Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-14B-Elite2&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
-
-|      Metric       |Value (%)|
-|-------------------|--------:|
-|**Average**        |    40.25|
-|IFEval (0-Shot)    |    61.76|
-|BBH (3-Shot)       |    46.81|
-|MATH Lvl 5 (4-Shot)|    46.90|
-|GPQA (0-shot)      |    16.00|
-|MuSR (0-shot)      |    22.24|
+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- opus
+- elite
+- calcium
+- trl
+- qwen
+model-index:
+- name: Calcium-Opus-14B-Elite2
+  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: 61.76
+      name: averaged accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
+      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: 46.81
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
+      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: 36.1
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
+      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: 16
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
+      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: 22.24
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
+      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: 47.79
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2
+      name: Open LLM Leaderboard
+---
+
+![e2.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CbSQZghlvWbMo2EdMacXp.gif)
+
+# **Calcium-Opus-14B-Elite2**
+
+Calcium-Opus-14B-Elite2 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. These models have proven effective in context understanding, reasoning, and mathematical problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets, with a focus on chain-of-thought (CoT) reasoning for problem-solving. 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.
+
+Key improvements include:  
+1. **Enhanced Knowledge and Expertise**: The model demonstrates significantly more knowledge and greatly improved capabilities in coding and mathematics, thanks to specialized expert models in these domains.  
+2. **Improved Instruction Following**: It shows significant advancements in following instructions, generating long texts (over 8K tokens), understanding structured data (e.g., tables), and producing structured outputs, especially in JSON format.  
+3. **Better Adaptability**: The model is more resilient to diverse system prompts, enabling enhanced role-playing implementations and condition-setting for chatbots.  
+4. **Long-Context Support**: It offers long-context support of up to 128K tokens and can generate up to 8K tokens in a single output.  
+5. **Multilingual Proficiency**: The model supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+# **Quickstart with transformers**
+
+Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate content:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Calcium-Opus-14B-Elite2"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"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**  
+1. **Reasoning and Context Understanding**:  
+   Designed to assist with complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction and critical thinking.  
+
+2. **Mathematical Problem-Solving**:  
+   Specialized for performing advanced mathematical reasoning and calculations, making it suitable for educational, scientific, and research-oriented applications.  
+
+3. **Code Generation and Debugging**:  
+   Offers robust support for coding tasks, including writing, debugging, and optimizing code in various programming languages, ideal for developers and software engineers.  
+
+4. **Structured Data Analysis**:  
+   Excels in processing and analyzing structured data, such as tables and JSON, and generating structured outputs, which is useful for data analysts and automation workflows.  
+
+5. **Multilingual Applications**:  
+   Supports over 29 languages, making it versatile for global applications like multilingual chatbots, content generation, and translations.  
+
+6. **Extended Content Generation**:  
+   Capable of generating long-form content (over 8K tokens), useful for writing reports, articles, and creating detailed instructional guides.  
+
+# **Limitations**  
+1. **Hardware Requirements**:  
+   Due to its 20B parameter size and support for long-context inputs, running the model requires significant computational resources, including high-memory GPUs or TPUs.  
+
+2. **Potential Bias in Multilingual Outputs**:  
+   While it supports 29 languages, the quality and accuracy of outputs may vary depending on the language, especially for less-resourced languages.  
+
+3. **Inconsistent Outputs for Creative Tasks**:  
+   The model may occasionally produce inconsistent or repetitive results in creative writing, storytelling, or highly subjective tasks.  
+
+4. **Limited Real-World Awareness**:  
+   It lacks real-time knowledge of current events beyond its training cutoff, which may limit its ability to respond accurately to the latest information.  
+
+5. **Error Propagation in Long-Text Outputs**:  
+   In generating long texts, minor errors in early outputs can sometimes propagate, reducing the overall coherence and accuracy of the response.  
+
+6. **Dependency on High-Quality Prompts**:  
+   Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.  
+
+# [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__Calcium-Opus-14B-Elite2-details)!
+Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-14B-Elite2&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
+
+|      Metric       |Value (%)|
+|-------------------|--------:|
+|**Average**        |    40.25|
+|IFEval (0-Shot)    |    61.76|
+|BBH (3-Shot)       |    46.81|
+|MATH Lvl 5 (4-Shot)|    46.90|
+|GPQA (0-shot)      |    16.00|
+|MuSR (0-shot)      |    22.24|
 |MMLU-PRO (5-shot)  |    47.79|