| | --- |
| | base_model: Spestly/Athena-2-1.5B |
| | tags: |
| | - text-generation-inference |
| | - transformers |
| | - unsloth |
| | - qwen2 |
| | - trl |
| | license: apache-2.0 |
| | language: |
| | - en |
| | library_name: transformers |
| | --- |
| |  |
| |
|
| | # AwA - 1.5B |
| |
|
| | 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. |
| |
|
| | ## Key Features |
| |
|
| | - **Chain-of-Thought Reasoning:** AwA delivers step-by-step breakdowns of solutions, mimicking logical human thought processes. |
| |
|
| | - **Domain Versatility:** Performs exceptionally across a wide range of domains, including mathematics, science, literature, and more. |
| |
|
| | - **Adaptive Responses:** Adjusts answer depth and complexity based on input queries, catering to both novices and experts. |
| |
|
| | - **Interactive Design:** Designed for educational tools, research assistants, and decision-making systems. |
| |
|
| | ## Intended Use Cases |
| |
|
| | - **Educational Applications:** Supports learning by breaking down complex problems into manageable steps. |
| |
|
| | - **Research Assistance:** Generates structured insights and explanations in academic or professional research. |
| |
|
| | - **Decision Support:** Enhances understanding in business, engineering, and scientific contexts. |
| |
|
| | - **General Inquiry:** Provides coherent, in-depth answers to everyday questions. |
| |
|
| | # Type: Chain-of-Thought (CoT) Reasoning Model |
| |
|
| | - Base Architecture: Adapted from [qwen2] |
| |
|
| | - Parameters: [1.54B] |
| |
|
| | - Fine-tuning: Specialized fine-tuning on Chain-of-Thought reasoning datasets to enhance step-by-step explanatory capabilities. |
| |
|
| |
|
| |
|
| | ## Ethical Considerations |
| |
|
| | - **Bias Mitigation:** I have taken steps to minimise biases in the training data. However, users are encouraged to cross-verify outputs in sensitive contexts. |
| |
|
| | - **Limitations:** May not provide exhaustive answers for niche topics or domains outside its training scope. |
| |
|
| | - **User Responsibility:** Designed as an assistive tool, not a replacement for expert human judgment. |
| |
|
| |
|
| | ## Usage |
| |
|
| | ### Option A: Local |
| |
|
| | Using locally with the Transformers library |
| |
|
| | ```python |
| | # Use a pipeline as a high-level helper |
| | from transformers import pipeline |
| | |
| | messages = [ |
| | {"role": "user", "content": "Who are you?"}, |
| | ] |
| | pipe = pipeline("text-generation", model="Spestly/AwA-1.5B") |
| | pipe(messages) |
| | ``` |
| |
|
| | ### Option B: API & Space |
| |
|
| | You can use the AwA HuggingFace space or the AwA API (Coming soon!) |
| |
|
| |
|
| | ## Roadmap |
| |
|
| | - More AwA model sizes e.g 7B and 14B |
| | - Create AwA API via spestly package |
