Update README.md

README.md

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 ⚡ Lightweight and Efficient
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 Compact Size: At just 3.09 billion parameters, Athena-1 3B offers excellent performance with reduced computational requirements.
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 Instruction Following: Fine-tuned for precise and reliable adherence to user prompts.
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 Coding and Mathematics: Proficient in solving coding challenges and handling mathematical tasks.
 
 📖 Long-Context Understanding
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 Context Length: Supports up to 32,768 tokens, enabling the processing of moderately lengthy documents or conversations.
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 Token Generation: Can generate up to 8K tokens of output.
 
 🌍 Multilingual Support
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 📊 Structured Data & Outputs
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 Structured Data Interpretation: Processes structured formats like tables and JSON.
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 Structured Output Generation: Generates well-formatted outputs, including JSON and other structured formats.
 
 Details
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 Base Model: Qwen/Qwen2.5-3B-Instruct
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 Architecture: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
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 Parameters: 3.09B total (2.77B non-embedding).
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 Layers: 36
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 Attention Heads: 16 for Q, 2 for KV.
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 Context Length: Up to 32,768 tokens.
 
 Applications
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 Athena 3B is designed for a variety of real-world applications:
 
 Conversational AI: Build fast, responsive, and lightweight chatbots.
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 Code Generation: Generate, debug, or explain code snippets.
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 Mathematical Problem Solving: Assist with calculations and reasoning.
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 Document Processing: Summarize and analyze moderately large documents.
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 Multilingual Applications: Support for global use cases with diverse language requirements.
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 Structured Data: Process and generate structured data, such as tables and JSON.
 
 Quickstart