Spestly/Athena-1-0.5B

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. Designed for ultra-lightweight applications, Athena-1 0.5B balances compactness with robust performance, making it suitable for tasks with limited computational resources.

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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.

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Model Details

• Base Model: 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.

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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.

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Quickstart

Here’s how you can use Athena-1 0.5B for quick text generation:

# 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