Improve language tag

README.md

@@ -1,91 +1,103 @@
----
-base_model: Qwen/Qwen2.5-0.5B-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 0.5B:
-
-Athena-1 0.5B is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). Designed for ultra-lightweight applications, Athena-1 0.5B balances compactness with robust performance, making it suitable for tasks with limited computational resources.
-
----
-
-## Key Features
-
-### ⚡ Ultra-Lightweight and Efficient
-
-*   **Compact Size:** With just **500 million parameters**, Athena-1 0.5B is ideal for edge devices and low-resource environments.
-*   **Instruction Following:** Fine-tuned for reliable adherence to user instructions.
-*   **Coding and Mathematics:** Capable of handling basic coding and mathematical tasks.
-
-### 📖 Contextual Understanding
-
-*   **Context Length:** Supports up to **16,384 tokens**, enabling processing of moderately sized conversations or documents.
-*   **Token Generation:** Can generate up to **4K tokens** of coherent output.
-
-### 🌍 Multilingual Support
-
-*   Supports **20+ languages**, including:
-    *   English, Chinese, French, Spanish, German, Italian, Russian
-    *   Japanese, Korean, Vietnamese, Thai, and more.
-
-### 📊 Structured Data & Outputs
-
-*   **Structured Data Interpretation:** Handles formats like tables and JSON effectively.
-*   **Structured Output Generation:** Produces well-formatted outputs for data-specific tasks.
-
----
-
-## Model Details
-
-*   **Base Model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
-*   **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
-*   **Parameters:** 500M total.
-*   **Layers:** (Adjust if different from the base model)
-*   **Attention Heads:** (Adjust if different from the base model)
-*   **Context Length:** Up to **16,384 tokens**.
-
----
-
-## Applications
-
-Athena-1 0.5B is optimized for:
-
-*   **Conversational AI:** Power lightweight and responsive chatbots.
-*   **Code Assistance:** Basic code generation, debugging, and explanations.
-*   **Mathematical Assistance:** Solves fundamental math problems.
-*   **Document Processing:** Summarizes and analyzes smaller documents effectively.
-*   **Multilingual Tasks:** Supports global use cases with a compact model.
-*   **Structured Data:** Reads and generates structured formats like JSON and tables.
-
----
-
-## Quickstart
-
-Here’s how you can use Athena-1 0.5B for quick text generation:
-
-```python
-# Use a pipeline as a high-level helper
-from transformers import pipeline
-
-messages = [
-    {"role": "user", "content": "What can you do?"},
-]
-pipe = pipeline("text-generation", model="Spestly/Athena-1-0.5B") # Update model name
-print(pipe(messages))
-
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-0.5B") # Update model name
-model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-0.5B") # Update model name
+---
+base_model: Qwen/Qwen2.5-0.5B-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 0.5B:
+
+Athena-1 0.5B is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). Designed for ultra-lightweight applications, Athena-1 0.5B balances compactness with robust performance, making it suitable for tasks with limited computational resources.
+
+---
+
+## Key Features
+
+### ⚡ Ultra-Lightweight and Efficient
+
+*   **Compact Size:** With just **500 million parameters**, Athena-1 0.5B is ideal for edge devices and low-resource environments.
+*   **Instruction Following:** Fine-tuned for reliable adherence to user instructions.
+*   **Coding and Mathematics:** Capable of handling basic coding and mathematical tasks.
+
+### 📖 Contextual Understanding
+
+*   **Context Length:** Supports up to **16,384 tokens**, enabling processing of moderately sized conversations or documents.
+*   **Token Generation:** Can generate up to **4K tokens** of coherent output.
+
+### 🌍 Multilingual Support
+
+*   Supports **20+ languages**, including:
+    *   English, Chinese, French, Spanish, German, Italian, Russian
+    *   Japanese, Korean, Vietnamese, Thai, and more.
+
+### 📊 Structured Data & Outputs
+
+*   **Structured Data Interpretation:** Handles formats like tables and JSON effectively.
+*   **Structured Output Generation:** Produces well-formatted outputs for data-specific tasks.
+
+---
+
+## Model Details
+
+*   **Base Model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+*   **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
+*   **Parameters:** 500M total.
+*   **Layers:** (Adjust if different from the base model)
+*   **Attention Heads:** (Adjust if different from the base model)
+*   **Context Length:** Up to **16,384 tokens**.
+
+---
+
+## Applications
+
+Athena-1 0.5B is optimized for:
+
+*   **Conversational AI:** Power lightweight and responsive chatbots.
+*   **Code Assistance:** Basic code generation, debugging, and explanations.
+*   **Mathematical Assistance:** Solves fundamental math problems.
+*   **Document Processing:** Summarizes and analyzes smaller documents effectively.
+*   **Multilingual Tasks:** Supports global use cases with a compact model.
+*   **Structured Data:** Reads and generates structured formats like JSON and tables.
+
+---
+
+## Quickstart
+
+Here’s how you can use Athena-1 0.5B for quick text generation:
+
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+messages = [
+    {"role": "user", "content": "What can you do?"},
+]
+pipe = pipeline("text-generation", model="Spestly/Athena-1-0.5B") # Update model name
+print(pipe(messages))
+
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-0.5B") # Update model name
+model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-0.5B") # Update model name
 ```