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--- |
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base_model: unsloth/qwen2.5-14b-instruct-bnb-4bit |
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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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# Athena 1: |
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Athena 1 is a state-of-the-art language model fine-tuned from [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct). Designed to excel in instruction-following tasks, Athena 1 delivers advanced capabilities in text generation, coding, mathematics, and long-context understanding. It is optimized for a wide variety of use cases, including conversational AI, structured data interpretation, and multilingual applications. It outperforms Ava 1.5 in many aspects making Athena-1 the superior model. |
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--- |
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## Key Features |
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### 🚀 Enhanced Capabilities |
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- **Instruction Following**: Athena 1 has been fine-tuned for superior adherence to user prompts, making it ideal for chatbots, virtual assistants, and guided workflows. |
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- **Coding and Mathematics**: Specialized fine-tuning enhances coding problem-solving and mathematical reasoning. |
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- **Long-Context Understanding**: Handles input contexts up to 128K tokens and generates up to 8K tokens. |
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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, XML, and other structured formats. |
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--- |
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## Model Details |
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- **Base Model**: [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) |
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- **Architecture**: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. |
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- **Parameters**: 14.7B total (13.1B non-embedding). |
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- **Layers**: 48 |
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- **Attention Heads**: 40 for Q, 8 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 wide range of use cases: |
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- Conversational AI and chatbots. |
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- Code generation, debugging, and explanation. |
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- Mathematical problem-solving. |
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- Large-document summarization and analysis. |
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- Multilingual text generation and translation. |
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- Structured data processing (e.g., tables, JSON). |
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--- |
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## Quickstart |
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Below is an example of how to use Athena 1 for text generation: |
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```python |
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huggingface-cli login |
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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-1-14B") |
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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-1-14B") |
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model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-14B") |
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``` |
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## Performance |
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Athena 1 has been optimized for efficiency and performance on modern GPUs. For detailed evaluation metrics (e.g., throughput, accuracy, and memory requirements), refer to the Qwen2.5 performance benchmarks. |
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--- |
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## Requirements |
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To use Athena 1, ensure the following: |
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- Python >= 3.8 |
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- Transformers >= 4.37.0 (to support Qwen models) |
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- PyTorch >= 2.0 |
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- GPU with BF16 support for optimal performance. |
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## Citation |
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If you use Athena 1 in your research or projects, please cite its base model Qwen2.5 as follows: |
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``` |
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@misc{qwen2.5, |
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title = {Qwen2.5: A Party of Foundation Models}, |
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url = {https://qwenlm.github.io/blog/qwen2.5/}, |
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author = {Qwen Team}, |
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month = {September}, |
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year = {2024} |
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} |
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``` |
