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Spestly / Athena-1-3B
Model's Last Updated: January 26 2025
text-generation
Introduction of Athena-1-3B
Model Details of Athena-1-3B
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")
Runs of Spestly Athena-1-3B on huggingface.co
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