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

# 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