Athena-1 3B:
Athena-1 3B is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-3B-Instruct. It is designed to provide efficient, high-quality text generation while maintaining a compact size. Athena 3B is optimized for lightweight applications, conversational AI, and structured data tasks, making it ideal for real-world use cases where performance and resource efficiency are critical.
Key Features
⚡ Lightweight and Efficient
- Compact Size: At just 3.09 billion parameters, Athena-1 3B 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.
Model Details
- Base Model: Qwen/Qwen2.5-3B-Instruct
- Architecture: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
- Parameters: 3.09B total (2.77B non-embedding).
- Layers: 36
- Attention Heads: 16 for Q, 2 for KV.
- Context Length: Up to 32,768 tokens.
Applications
Athena 3B 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.
Quickstart
Here’s how you can use Athena 3B 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-1-3B")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-3B")
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-3B")