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---
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# 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](https://huggingface.co/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](https://huggingface.co/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:
```python
# 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")
``` |