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---
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Athena-1 1.5B:
Athena-1 1.5B is a fine-tuned, instruction-following large language model derived from [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). Designed for efficiency and high-quality text generation, Athena-1 1.5B maintains a compact size, making it ideal for real-world applications where performance and resource efficiency are critical, such as lightweight applications, conversational AI, and structured data tasks.
---
## Key Features
### ⚡ Lightweight and Efficient
* **Compact Size:** At just **1.5 billion parameters**, Athena-1 1.5B 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/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
* **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
* **Parameters:** 1.5B total (Adjust non-embedding count if you have it).
* **Layers:** (Adjust if different from the 3B model)
* **Attention Heads:** (Adjust if different from the 3B model)
* **Context Length:** Up to **32,768 tokens**.
---
## Applications
Athena 1.5B 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 1.5B 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-1-1.5B") # Update model name
print(pipe(messages))
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-1.5B") # Update model name
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-1.5B") # Update model name
``` |