aayanmishra-ml/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.

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
• —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:

# 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