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