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---
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 |