Spestly/Athena-1-1.5B

Athena-1 1.5B:

Athena-1 1.5B is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-1.5B-Instruct. Designed for efficiency and high-quality text generation, Athena-1 1.5B maintains a compact size, making it ideal for real-world applications where performance and resource efficiency are critical, such as lightweight applications, conversational AI, and structured data tasks.

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Key Features

⚡ Lightweight and Efficient

• Compact Size: At just 1.5 billion parameters, Athena-1 1.5B offers excellent performance with reduced computational requirements.
• Instruction Following: Fine-tuned for precise and reliable adherence to user prompts.
• Coding and Mathematics: Proficient in solving coding challenges and handling mathematical tasks.

📖 Long-Context Understanding

• Context Length: Supports up to 32,768 tokens, enabling the processing of moderately lengthy 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: Processes structured formats like tables and JSON.
• Structured Output Generation: Generates well-formatted outputs, including JSON and other structured formats.

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Model Details

• Base Model: Qwen/Qwen2.5-1.5B-Instruct
• Architecture: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
• Parameters: 1.5B total (Adjust non-embedding count if you have it).
• Layers: (Adjust if different from the 3B model)
• Attention Heads: (Adjust if different from the 3B model)
• Context Length: Up to 32,768 tokens.

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Applications

Athena 1.5B is designed for a variety of real-world applications:

• Conversational AI: Build fast, responsive, and lightweight chatbots.
• Code Generation: Generate, debug, or explain code snippets.
• Mathematical Problem Solving: Assist with calculations and reasoning.
• Document Processing: Summarize and analyze moderately large documents.
• Multilingual Applications: Support for global use cases with diverse language requirements.
• Structured Data: Process and generate structured data, such as tables and JSON.

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Quickstart

Here’s how you can use Athena 1.5B for quick text generation:

# 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-1-1.5B") # Update model name
print(pipe(messages))

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-1.5B") # Update model name
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-1.5B") # Update model name