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.

README.md

@@ -1,218 +1,229 @@
----
-license: apache-2.0
-language:
-- en
-- zh
-base_model:
-- Qwen/Qwen2.5-14B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- text-generation-inference
-- math
-- code
-- trl
-- sft
-model-index:
-- name: Epimetheus-14B-Axo
-  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: 55.46
-      name: averaged accuracy
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
-      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: 51.46
-      name: normalized accuracy
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
-      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: 41.01
-      name: exact match
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
-      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: 19.02
-      name: acc_norm
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
-      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: 19.71
-      name: acc_norm
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
-      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: 47.82
-      name: accuracy
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
-      name: Open LLM Leaderboard
----
-![aszsdcazss.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jU62zQyKIuTfwXewv4T0Y.png)
-
-# **Epimetheus-14B-Axo**  
-
-> Epimetheus-14B-Axo is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.  
-
-## **Key Improvements**  
-1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses.  
-2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions.  
-3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries.  
-4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.  
-5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, 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 generate content:  
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "prithivMLmods/Epimetheus-14B-Axo"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "What are the key principles of general-purpose AI?"
-messages = [
-    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
-    {"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. **General-Purpose Reasoning**:  
-   Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems.  
-
-2. **Educational and Informational Assistance**:  
-   Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users.  
-
-3. **Conversational AI and Chatbots**:  
-   Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation.  
-
-4. **Multilingual Applications**:  
-   Supports global communication, translations, and multilingual content generation.  
-
-5. **Structured Data Processing**:  
-   Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation.  
-
-6. **Long-Form Content Generation**:  
-   Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs.  
-
-## **Limitations**  
-1. **Hardware Requirements**:  
-   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  
-
-2. **Potential Bias in Responses**:  
-   While designed to be neutral, outputs may still reflect biases present in training data.  
-
-3. **Inconsistent Outputs in Creative Tasks**:  
-   May produce variable results in storytelling and highly subjective topics.  
-
-4. **Limited Real-World Awareness**:  
-   Does not have access to real-time events beyond its training cutoff.  
-
-5. **Error Propagation in Extended Outputs**:  
-   Minor errors in early responses may affect overall coherence in long-form outputs.  
-
-6. **Prompt Sensitivity**:  
-   The effectiveness of responses may depend on how well the input prompt is structured.
-# [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__Epimetheus-14B-Axo-details)!
-Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FEpimetheus-14B-Axo&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
-
-|      Metric       |Value (%)|
-|-------------------|--------:|
-|**Average**        |    39.08|
-|IFEval (0-Shot)    |    55.46|
-|BBH (3-Shot)       |    51.46|
-|MATH Lvl 5 (4-Shot)|    41.01|
-|GPQA (0-shot)      |    19.02|
-|MuSR (0-shot)      |    19.71|
-|MMLU-PRO (5-shot)  |    47.82|
-
+---
+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:
+- text-generation-inference
+- math
+- code
+- trl
+- sft
+model-index:
+- name: Epimetheus-14B-Axo
+  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: 55.46
+      name: averaged accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
+      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: 51.46
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
+      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: 41.01
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
+      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: 19.02
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
+      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: 19.71
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
+      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: 47.82
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FEpimetheus-14B-Axo
+      name: Open LLM Leaderboard
+---
+![aszsdcazss.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jU62zQyKIuTfwXewv4T0Y.png)
+
+# **Epimetheus-14B-Axo**  
+
+> Epimetheus-14B-Axo is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.  
+
+## **Key Improvements**  
+1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses.  
+2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions.  
+3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries.  
+4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.  
+5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, 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 generate content:  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Epimetheus-14B-Axo"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "What are the key principles of general-purpose AI?"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
+    {"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. **General-Purpose Reasoning**:  
+   Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems.  
+
+2. **Educational and Informational Assistance**:  
+   Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users.  
+
+3. **Conversational AI and Chatbots**:  
+   Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation.  
+
+4. **Multilingual Applications**:  
+   Supports global communication, translations, and multilingual content generation.  
+
+5. **Structured Data Processing**:  
+   Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation.  
+
+6. **Long-Form Content Generation**:  
+   Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs.  
+
+## **Limitations**  
+1. **Hardware Requirements**:  
+   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  
+
+2. **Potential Bias in Responses**:  
+   While designed to be neutral, outputs may still reflect biases present in training data.  
+
+3. **Inconsistent Outputs in Creative Tasks**:  
+   May produce variable results in storytelling and highly subjective topics.  
+
+4. **Limited Real-World Awareness**:  
+   Does not have access to real-time events beyond its training cutoff.  
+
+5. **Error Propagation in Extended Outputs**:  
+   Minor errors in early responses may affect overall coherence in long-form outputs.  
+
+6. **Prompt Sensitivity**:  
+   The effectiveness of responses may depend on how well the input prompt is structured.
+# [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__Epimetheus-14B-Axo-details)!
+Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FEpimetheus-14B-Axo&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
+
+|      Metric       |Value (%)|
+|-------------------|--------:|
+|**Average**        |    39.08|
+|IFEval (0-Shot)    |    55.46|
+|BBH (3-Shot)       |    51.46|
+|MATH Lvl 5 (4-Shot)|    41.01|
+|GPQA (0-shot)      |    19.02|
+|MuSR (0-shot)      |    19.71|
+|MMLU-PRO (5-shot)  |    47.82|
+