---
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](https://raw.githubusercontent.com/Aayan-Mishra/Images/refs/heads/main/Athena.png)

# Athena-1 3B:

Athena-1 3B is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/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](https://huggingface.co/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:

```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-1-3B")
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")
```