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Athena-1-3B

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Athena-1-3B / README.md
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metadata
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - qwen2
  - trl
license: other
license_name: qwen-research
license_link: https://huggingface.co/Spestly/Athena-1-3B/blob/main/LICENSE
language:
  - en

Header

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")