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--- |
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base_model: Qwen/Qwen2.5-7B-Instruct |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen2 |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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 |
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# Athena-1: Lightweight and Powerful Instruction-Following Model |
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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. |
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--- |
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## Key Features |
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### 🚀 Enhanced Performance |
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- **Instruction Following**: Fine-tuned for excellent adherence to user prompts and instructions. |
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- **Coding and Mathematics**: Proficient in solving coding problems and mathematical reasoning. |
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- **Lightweight**: At 7.62 billion parameters, Athena-1-7B offers powerful performance while maintaining efficiency. |
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### 📖 Long-Context Understanding |
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- **Context Length**: Supports up to **128K tokens**, ensuring accurate handling of large documents or conversations. |
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- **Token Generation**: Can generate up to **8K tokens** of output. |
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### 🌍 Multilingual Support |
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- Supports **29+ languages**, including: |
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- English, Chinese, French, Spanish, Portuguese, German, Italian, Russian |
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- Japanese, Korean, Vietnamese, Thai, Arabic, and more. |
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### 📊 Structured Data & Outputs |
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- **Structured Data Interpretation**: Understands and processes structured formats like tables and JSON. |
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- **Structured Output Generation**: Generates well-formatted outputs, including JSON and other structured formats. |
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--- |
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## Model Details |
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- **Base Model**: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
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- **Architecture**: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. |
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- **Parameters**: 7.62B total (6.53B non-embedding). |
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- **Layers**: 28 |
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- **Attention Heads**: 28 for Q, 4 for KV. |
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- **Context Length**: Up to **131,072 tokens**. |
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--- |
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## Applications |
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Athena-1 is designed for a broad range of use cases: |
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- **Conversational AI**: Create natural, human-like chatbot experiences. |
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- **Code Generation**: Generate, debug, or explain code snippets. |
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- **Mathematical Problem Solving**: Assist with complex calculations and reasoning. |
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- **Document Processing**: Summarize or analyze large documents. |
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- **Multilingual Applications**: Support for diverse languages for translation and global use cases. |
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- **Structured Data**: Process and generate structured data, including tables and JSON. |
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--- |
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## Quickstart |
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Here’s how you can use Athena 7B for quick text generation: |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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messages = [ |
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{"role": "user", "content": "Who are you?"}, |
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] |
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pipe = pipeline("text-generation", model="Spestly/Athena-1-7B") |
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pipe(messages) |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-7B") |
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model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-7B") |
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``` |
