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--- |
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base_model: Spestly/Athena-2-1.5B |
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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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library_name: transformers |
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--- |
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# AwA - 1.5B |
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AwA (Answers with Athena) is my portfolio project, showcasing a cutting-edge Chain-of-Thought (CoT) reasoning model. I created AwA to excel in providing detailed, step-by-step answers to complex questions across diverse domains. This model represents my dedication to advancing AI’s capability for enhanced comprehension, problem-solving, and knowledge synthesis. |
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## Key Features |
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- **Chain-of-Thought Reasoning:** AwA delivers step-by-step breakdowns of solutions, mimicking logical human thought processes. |
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- **Domain Versatility:** Performs exceptionally across a wide range of domains, including mathematics, science, literature, and more. |
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- **Adaptive Responses:** Adjusts answer depth and complexity based on input queries, catering to both novices and experts. |
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- **Interactive Design:** Designed for educational tools, research assistants, and decision-making systems. |
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## Intended Use Cases |
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- **Educational Applications:** Supports learning by breaking down complex problems into manageable steps. |
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- **Research Assistance:** Generates structured insights and explanations in academic or professional research. |
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- **Decision Support:** Enhances understanding in business, engineering, and scientific contexts. |
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- **General Inquiry:** Provides coherent, in-depth answers to everyday questions. |
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# Type: Chain-of-Thought (CoT) Reasoning Model |
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- Base Architecture: Adapted from [qwen2] |
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- Parameters: [1.54B] |
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- Fine-tuning: Specialized fine-tuning on Chain-of-Thought reasoning datasets to enhance step-by-step explanatory capabilities. |
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## Ethical Considerations |
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- **Bias Mitigation:** I have taken steps to minimise biases in the training data. However, users are encouraged to cross-verify outputs in sensitive contexts. |
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- **Limitations:** May not provide exhaustive answers for niche topics or domains outside its training scope. |
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- **User Responsibility:** Designed as an assistive tool, not a replacement for expert human judgment. |
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## Usage |
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### Option A: Local |
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Using locally with the Transformers library |
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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/AwA-1.5B") |
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pipe(messages) |
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``` |
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### Option B: API & Space |
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You can use the AwA HuggingFace space or the AwA API (Coming soon!) |
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## Roadmap |
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- More AwA model sizes e.g 7B and 14B |
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- Create AwA API via spestly package |
