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

@@ -1,163 +1,146 @@
----
-base_model:
-- Qwen/Qwen2.5-14B-Instruct
-license: mit
-language:
-- en
-- zh
-- fr
-- es
-- pt
-- de
-- it
-- ru
-- ja
-- ko
-- vi
-- th
-- ar
-- fa
-- he
-- tr
-- cs
-- pl
-- hi
-- bn
-- ur
-- id
-- ms
-- lo
-- my
-- ceb
-- km
-- tl
-- nl
-tags:
-- chemistry
-- biology
-- code
-- text-generation-inference
-- STEM
-- unsloth
-- text-generation-inference
-- transformers
-- qwen2
-- trl
----
-<div align="center">
-<span style="font-family: default; font-size: 1.5em;">Athena-3</span>
-<div>
-🚀 Faster, Sharper, Smarter than Athena 1 and Athena 2🌟
-</div>
-</div>
-<br>
-<div align="center" style="line-height: 1;">
-  <a href="https://github.com/Aayan-Mishra/Maverick-Search" style="margin: 2px;">
-    <img alt="Github Page" src="https://img.shields.io/badge/Toolkit-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://aayanmishra.com/blog/athena-3" target="_blank" style="margin: 2px;">
-    <img alt="Blogpost" src="https://img.shields.io/badge/Blogpost-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://huggingface.co/Spestly/Athena-3-14B" style="margin: 2px;">
-    <img alt="HF Page" src="https://img.shields.io/badge/Athena-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-
-## **Athena-3**
-
-*Athena generated this model card!*
-
-**Athena-3-14B** is a 14.0-billion-parameter causal language model fine-tuned from Qwen2.5-14B-Instruct. This model is designed to provide highly fluent, contextually aware, and logically sound outputs across a broad range of NLP and reasoning tasks. It balances instruction-following with generative flexibility.
-
-## **Model Details**
-
-- **Model Developer:** Aayan Mishra
-- **Model Type:** Causal Language Model
-- **Architecture:** Transformer with Rotary Position Embeddings (RoPE), SwiGLU activation, RMSNorm, Attention QKV bias, and tied word embeddings
-- **Parameters:** 14.0 billion total (12.84 billion non-embedding)
-- **Layers:** 40
-- **Attention Heads:** 40 for query and 4 for key-value (Grouped Query Attention)
-- **Vocabulary Size:** Approximately 151,646 tokens
-- **Context Length:** Supports up to 131,072 tokens
-- **Languages Supported:** Over 29 languages, including strong performance in English, Chinese, and multilingual instruction tasks
-- **License:** MIT
-
-## **Training Details**
-
-Athena-3-14B was fine-tuned using the Unsloth framework on a single NVIDIA A100 GPU. The fine-tuning process spanned approximately 90 minutes over 60 epochs, utilizing a curated instruction-tuned dataset. It is tailored for generalist NLP performance with a focus on reasoning, alignment, and fluency.
-
-## **Intended Use**
-
-Athena-3-14B is ideal for a wide variety of tasks, including:
-
-- **Instruction Following:** Handling complex prompts with step-by-step logical output
-- **Writing Assistance:** Generating essays, emails, and coherent narratives
-- **NLP Tasks:** Summarization, question answering, translation, and text classification
-- **STEM Support:** Reasoning through academic and technical content
-
-While Athena-3-14B is a versatile model, it is not intended for safety-critical applications or the handling of private, sensitive information.
-
-## **How to Use**
-
-To utilize Athena-3-14B, ensure that you have the latest version of the `transformers` library installed:
-
-```bash
-pip install transformers
-```
-
-Here's an example of how to load the Athena-3-14B model and generate a response:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "Spestly/Athena-3-14B"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-prompt = "Explain the concept of entropy in thermodynamics."
-messages = [
-    {"role": "system", "content": "You are Maverick, an AI assistant designed to be helpful."},
-    {"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]
-print(response)
-```
-
-### **Maverick Search usage 🔍**
-
-To use this model with Maverick Search, please refer to this [repository](https://github.com/Aayan-Mishra/Maverick-Search)
-
-## **Limitations**
-
-Users should be aware of the following limitations:
-
-- **Biases:** Athena-3-14B may reflect biases from its pretraining and fine-tuning data. Outputs should be reviewed for fairness and accuracy.
-- **Knowledge Cutoff:** The model's knowledge is current as of August 2024.
-- **Multilingual Performance:** Performance varies by language, with strongest capabilities in English and aligned datasets.
-
-## **Acknowledgements**
-
-Athena-3-14B builds upon the Qwen2.5-14B foundation. Special thanks to the open-source ecosystem and Unsloth for enabling efficient fine-tuning workflows.
-
-## **License**
-
-Athena-3-14B is released under the MIT License, permitting broad use and distribution with proper attribution.
-
-## **Contact**
-
+---
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+license: mit
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+tags:
+- chemistry
+- biology
+- code
+- text-generation-inference
+- STEM
+- unsloth
+- transformers
+- qwen2
+- trl
+---
+<div align="center">
+<span style="font-family: default; font-size: 1.5em;">Athena-3</span>
+<div>
+🚀 Faster, Sharper, Smarter than Athena 1 and Athena 2🌟
+</div>
+</div>
+<br>
+<div align="center" style="line-height: 1;">
+  <a href="https://github.com/Aayan-Mishra/Maverick-Search" style="margin: 2px;">
+    <img alt="Github Page" src="https://img.shields.io/badge/Toolkit-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://aayanmishra.com/blog/athena-3" target="_blank" style="margin: 2px;">
+    <img alt="Blogpost" src="https://img.shields.io/badge/Blogpost-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/Spestly/Athena-3-14B" style="margin: 2px;">
+    <img alt="HF Page" src="https://img.shields.io/badge/Athena-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## **Athena-3**
+
+*Athena generated this model card!*
+
+**Athena-3-14B** is a 14.0-billion-parameter causal language model fine-tuned from Qwen2.5-14B-Instruct. This model is designed to provide highly fluent, contextually aware, and logically sound outputs across a broad range of NLP and reasoning tasks. It balances instruction-following with generative flexibility.
+
+## **Model Details**
+
+- **Model Developer:** Aayan Mishra
+- **Model Type:** Causal Language Model
+- **Architecture:** Transformer with Rotary Position Embeddings (RoPE), SwiGLU activation, RMSNorm, Attention QKV bias, and tied word embeddings
+- **Parameters:** 14.0 billion total (12.84 billion non-embedding)
+- **Layers:** 40
+- **Attention Heads:** 40 for query and 4 for key-value (Grouped Query Attention)
+- **Vocabulary Size:** Approximately 151,646 tokens
+- **Context Length:** Supports up to 131,072 tokens
+- **Languages Supported:** Over 29 languages, including strong performance in English, Chinese, and multilingual instruction tasks
+- **License:** MIT
+
+## **Training Details**
+
+Athena-3-14B was fine-tuned using the Unsloth framework on a single NVIDIA A100 GPU. The fine-tuning process spanned approximately 90 minutes over 60 epochs, utilizing a curated instruction-tuned dataset. It is tailored for generalist NLP performance with a focus on reasoning, alignment, and fluency.
+
+## **Intended Use**
+
+Athena-3-14B is ideal for a wide variety of tasks, including:
+
+- **Instruction Following:** Handling complex prompts with step-by-step logical output
+- **Writing Assistance:** Generating essays, emails, and coherent narratives
+- **NLP Tasks:** Summarization, question answering, translation, and text classification
+- **STEM Support:** Reasoning through academic and technical content
+
+While Athena-3-14B is a versatile model, it is not intended for safety-critical applications or the handling of private, sensitive information.
+
+## **How to Use**
+
+To utilize Athena-3-14B, ensure that you have the latest version of the `transformers` library installed:
+
+```bash
+pip install transformers
+```
+
+Here's an example of how to load the Athena-3-14B model and generate a response:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "Spestly/Athena-3-14B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "Explain the concept of entropy in thermodynamics."
+messages = [
+    {"role": "system", "content": "You are Maverick, an AI assistant designed to be helpful."},
+    {"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]
+print(response)
+```
+
+### **Maverick Search usage 🔍**
+
+To use this model with Maverick Search, please refer to this [repository](https://github.com/Aayan-Mishra/Maverick-Search)
+
+## **Limitations**
+
+Users should be aware of the following limitations:
+
+- **Biases:** Athena-3-14B may reflect biases from its pretraining and fine-tuning data. Outputs should be reviewed for fairness and accuracy.
+- **Knowledge Cutoff:** The model's knowledge is current as of August 2024.
+- **Multilingual Performance:** Performance varies by language, with strongest capabilities in English and aligned datasets.
+
+## **Acknowledgements**
+
+Athena-3-14B builds upon the Qwen2.5-14B foundation. Special thanks to the open-source ecosystem and Unsloth for enabling efficient fine-tuning workflows.
+
+## **License**
+
+Athena-3-14B is released under the MIT License, permitting broad use and distribution with proper attribution.
+
+## **Contact**
+
 - Email: [email protected]