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--- |
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base_model: Qwen/Qwen2.5-3B-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: other |
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license_name: qwen-research |
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license_link: https://huggingface.co/Spestly/Athena-1-3B/blob/main/LICENSE |
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language: |
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- en |
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--- |
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# Athena-1 3B: |
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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. |
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--- |
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## Key Features |
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### ⚡ Lightweight and Efficient |
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- **Compact Size**: At just **3.09 billion parameters**, Athena-1 3B offers excellent performance with reduced computational requirements. |
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- **Instruction Following**: Fine-tuned for precise and reliable adherence to user prompts. |
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- **Coding and Mathematics**: Proficient in solving coding challenges and handling mathematical tasks. |
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### 📖 Long-Context Understanding |
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- **Context Length**: Supports up to **32,768 tokens**, enabling the processing of moderately lengthy 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**: 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/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) |
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- **Architecture**: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings. |
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- **Parameters**: 3.09B total (2.77B non-embedding). |
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- **Layers**: 36 |
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- **Attention Heads**: 16 for Q, 2 for KV. |
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- **Context Length**: Up to **32,768 tokens**. |
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--- |
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## Applications |
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Athena 3B is designed for a variety of real-world applications: |
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- **Conversational AI**: Build fast, responsive, and lightweight chatbots. |
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- **Code Generation**: Generate, debug, or explain code snippets. |
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- **Mathematical Problem Solving**: Assist with calculations and reasoning. |
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- **Document Processing**: Summarize and analyze moderately large documents. |
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- **Multilingual Applications**: Support for global use cases with diverse language requirements. |
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- **Structured Data**: Process and generate structured data, such as tables and JSON. |
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--- |
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## Quickstart |
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Here’s how you can use Athena 3B 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-1-3B") |
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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-1-3B") |
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model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-3B") |
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``` |
