Improve language tag

README.md

@@ -1,85 +1,97 @@
----
-base_model: Qwen/Qwen2.5-7B-Instruct
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-- trl
-license: apache-2.0
-language:
-- en
----
-![Header](https://raw.githubusercontent.com/Aayan-Mishra/Images/refs/heads/main/Athena.png)
-
-# Athena-1: Lightweight and Powerful Instruction-Following Model
-
-Athena-1 is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). Designed to balance efficiency and performance, Athena 7B provides powerful text-generation capabilities, making it suitable for a variety of real-world applications, including conversational AI, content creation, and structured data processing.
-
----
-
-## Key Features
-
-### 🚀 Enhanced Performance
-- **Instruction Following**: Fine-tuned for excellent adherence to user prompts and instructions.
-- **Coding and Mathematics**: Proficient in solving coding problems and mathematical reasoning.
-- **Lightweight**: At 7.62 billion parameters, Athena-1-7B offers powerful performance while maintaining efficiency.
-
-### 📖 Long-Context Understanding
-- **Context Length**: Supports up to **128K tokens**, ensuring accurate handling of large documents or conversations.
-- **Token Generation**: Can generate up to **8K tokens** of output.
-
-### 🌍 Multilingual Support
-- Supports **29+ languages**, including:
-  - English, Chinese, French, Spanish, Portuguese, German, Italian, Russian
-  - Japanese, Korean, Vietnamese, Thai, Arabic, and more.
-
-### 📊 Structured Data & Outputs
-- **Structured Data Interpretation**: Understands and processes structured formats like tables and JSON.
-- **Structured Output Generation**: Generates well-formatted outputs, including JSON and other structured formats.
-
----
-
-## Model Details
-
-- **Base Model**: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
-- **Architecture**: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
-- **Parameters**: 7.62B total (6.53B non-embedding).
-- **Layers**: 28
-- **Attention Heads**: 28 for Q, 4 for KV.
-- **Context Length**: Up to **131,072 tokens**.
-
----
-
-## Applications
-
-Athena-1 is designed for a broad range of use cases:
-- **Conversational AI**: Create natural, human-like chatbot experiences.
-- **Code Generation**: Generate, debug, or explain code snippets.
-- **Mathematical Problem Solving**: Assist with complex calculations and reasoning.
-- **Document Processing**: Summarize or analyze large documents.
-- **Multilingual Applications**: Support for diverse languages for translation and global use cases.
-- **Structured Data**: Process and generate structured data, including tables and JSON.
-
----
-
-## Quickstart
-
-Here’s how you can use Athena 7B for quick text generation:
-
-```python
-# Use a pipeline as a high-level helper
-from transformers import pipeline
-
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
-pipe = pipeline("text-generation", model="Spestly/Athena-1-7B")
-pipe(messages)
-
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-7B")
-model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-7B")
+---
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+![Header](https://raw.githubusercontent.com/Aayan-Mishra/Images/refs/heads/main/Athena.png)
+
+# Athena-1: Lightweight and Powerful Instruction-Following Model
+
+Athena-1 is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). Designed to balance efficiency and performance, Athena 7B provides powerful text-generation capabilities, making it suitable for a variety of real-world applications, including conversational AI, content creation, and structured data processing.
+
+---
+
+## Key Features
+
+### 🚀 Enhanced Performance
+- **Instruction Following**: Fine-tuned for excellent adherence to user prompts and instructions.
+- **Coding and Mathematics**: Proficient in solving coding problems and mathematical reasoning.
+- **Lightweight**: At 7.62 billion parameters, Athena-1-7B offers powerful performance while maintaining efficiency.
+
+### 📖 Long-Context Understanding
+- **Context Length**: Supports up to **128K tokens**, ensuring accurate handling of large documents or conversations.
+- **Token Generation**: Can generate up to **8K tokens** of output.
+
+### 🌍 Multilingual Support
+- Supports **29+ languages**, including:
+  - English, Chinese, French, Spanish, Portuguese, German, Italian, Russian
+  - Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+### 📊 Structured Data & Outputs
+- **Structured Data Interpretation**: Understands and processes structured formats like tables and JSON.
+- **Structured Output Generation**: Generates well-formatted outputs, including JSON and other structured formats.
+
+---
+
+## Model Details
+
+- **Base Model**: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+- **Architecture**: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
+- **Parameters**: 7.62B total (6.53B non-embedding).
+- **Layers**: 28
+- **Attention Heads**: 28 for Q, 4 for KV.
+- **Context Length**: Up to **131,072 tokens**.
+
+---
+
+## Applications
+
+Athena-1 is designed for a broad range of use cases:
+- **Conversational AI**: Create natural, human-like chatbot experiences.
+- **Code Generation**: Generate, debug, or explain code snippets.
+- **Mathematical Problem Solving**: Assist with complex calculations and reasoning.
+- **Document Processing**: Summarize or analyze large documents.
+- **Multilingual Applications**: Support for diverse languages for translation and global use cases.
+- **Structured Data**: Process and generate structured data, including tables and JSON.
+
+---
+
+## Quickstart
+
+Here’s how you can use Athena 7B for quick text generation:
+
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+pipe = pipeline("text-generation", model="Spestly/Athena-1-7B")
+pipe(messages)
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-7B")
+model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-7B")
 ```