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---
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Athena-1 0.5B:
Athena-1 0.5B is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). Designed for ultra-lightweight applications, Athena-1 0.5B balances compactness with robust performance, making it suitable for tasks with limited computational resources.
---
## Key Features
### ⚡ Ultra-Lightweight and Efficient
* **Compact Size:** With just **500 million parameters**, Athena-1 0.5B is ideal for edge devices and low-resource environments.
* **Instruction Following:** Fine-tuned for reliable adherence to user instructions.
* **Coding and Mathematics:** Capable of handling basic coding and mathematical tasks.
### 📖 Contextual Understanding
* **Context Length:** Supports up to **16,384 tokens**, enabling processing of moderately sized conversations or documents.
* **Token Generation:** Can generate up to **4K tokens** of coherent output.
### 🌍 Multilingual Support
* Supports **20+ languages**, including:
* English, Chinese, French, Spanish, German, Italian, Russian
* Japanese, Korean, Vietnamese, Thai, and more.
### 📊 Structured Data & Outputs
* **Structured Data Interpretation:** Handles formats like tables and JSON effectively.
* **Structured Output Generation:** Produces well-formatted outputs for data-specific tasks.
---
## Model Details
* **Base Model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
* **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
* **Parameters:** 500M total.
* **Layers:** (Adjust if different from the base model)
* **Attention Heads:** (Adjust if different from the base model)
* **Context Length:** Up to **16,384 tokens**.
---
## Applications
Athena-1 0.5B is optimized for:
* **Conversational AI:** Power lightweight and responsive chatbots.
* **Code Assistance:** Basic code generation, debugging, and explanations.
* **Mathematical Assistance:** Solves fundamental math problems.
* **Document Processing:** Summarizes and analyzes smaller documents effectively.
* **Multilingual Tasks:** Supports global use cases with a compact model.
* **Structured Data:** Reads and generates structured formats like JSON and tables.
---
## Quickstart
Here’s how you can use Athena-1 0.5B for quick text generation:
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "What can you do?"},
]
pipe = pipeline("text-generation", model="Spestly/Athena-1-0.5B") # Update model name
print(pipe(messages))
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-0.5B") # Update model name
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-0.5B") # Update model name
``` |