Athena-1: Lightweight and Powerful Instruction-Following Model
Athena-1 is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-7B-Instruct. Designed to balance efficiency and performance, Athena 7B provides powerful text-generation capabilities, making it suitable for a variety of real-world applications, including conversational AI, content creation, and structured data processing.
Key Features
🚀 Enhanced Performance
- Instruction Following: Fine-tuned for excellent adherence to user prompts and instructions.
- Coding and Mathematics: Proficient in solving coding problems and mathematical reasoning.
- Lightweight: At 7.62 billion parameters, Athena-1-7B offers powerful performance while maintaining efficiency.
📖 Long-Context Understanding
- Context Length: Supports up to 128K tokens, ensuring accurate handling of large 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: Understands and processes structured formats like tables and JSON.
- Structured Output Generation: Generates well-formatted outputs, including JSON and other structured formats.
Model Details
- Base Model: Qwen/Qwen2.5-7B-Instruct
- Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
- Parameters: 7.62B total (6.53B non-embedding).
- Layers: 28
- Attention Heads: 28 for Q, 4 for KV.
- Context Length: Up to 131,072 tokens.
Applications
Athena-1 is designed for a broad range of use cases:
- Conversational AI: Create natural, human-like chatbot experiences.
- Code Generation: Generate, debug, or explain code snippets.
- Mathematical Problem Solving: Assist with complex calculations and reasoning.
- Document Processing: Summarize or analyze large documents.
- Multilingual Applications: Support for diverse languages for translation and global use cases.
- Structured Data: Process and generate structured data, including tables and JSON.
Quickstart
Here’s how you can use Athena 7B 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-1-7B")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-7B")
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-7B")