AetherLlama

• Developed by: Daemontatox
• License: Apache 2.0
• Finetuned Using: Unsloth, Hugging Face Transformers, and TRL Library

Model Overview

The ** AetherLlama Model** is an advanced AI system optimized for logical reasoning, multi-step problem-solving, and decision-making tasks. Designed with efficiency and accuracy in mind, it employs a structured system prompt to ensure high-quality answers through a transparent and iterative thought process.

System Prompt and Workflow

This model operates using an innovative reasoning framework structured around the following steps:

1. Initial Thought:
   The model uses <Thinking> tags to reason step-by-step and craft its best possible response.
   Example:

2. Self-Critique:
   It evaluates its initial response within <Critique> tags, focusing on:

• Accuracy: Is it factually correct and verifiable?
• Clarity: Is it clear and free of ambiguity?
• Completeness: Does it fully address the request?
• Improvement: What can be enhanced?
  Example:

3. Revision:
   Based on the critique, the model refines its response within <Revising> tags.
   Example:

4. Final Response:
   The revised response is presented clearly within <Final> tags.
   Example:

5. Tag Innovation:
   When needed, the model creates and defines new tags for better structuring or clarity, ensuring consistent usage.
   Example:

Key Features

• Structured Reasoning: Transparent, multi-step approach for generating and refining answers.
• Self-Improvement: Built-in critique and revision ensure continuous response enhancement.
• Clarity and Adaptability: Tagging system provides organized, adaptable responses tailored to user needs.
• Creative Flexibility: Supports dynamic problem-solving with the ability to introduce new tags and concepts.

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Use Cases

The model is designed for various domains, including:

1. Research and Analysis: Extracting insights and providing structured explanations.
2. Education: Assisting with tutoring by breaking down complex problems step-by-step.
3. Problem-Solving: Offering logical and actionable solutions for multi-step challenges.
4. Content Generation: Producing clear, well-organized creative or professional content.

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Training Details

• Frameworks:

• Unsloth for accelerated training.

• Hugging Face Transformers and the TRL library for reinforcement learning with human feedback (RLHF).

• Dataset: Finetuned on diverse reasoning-focused tasks, including logical puzzles, mathematical problems, and commonsense reasoning scenarios.

• Hardware Efficiency:

• Trained with bnb-4bit precision for reduced memory usage.

• Optimized training pipeline achieving 2x faster development cycles.

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Performance Metrics

The model excels in reasoning benchmarks:

• ARC (AI2 Reasoning Challenge): High accuracy in logical and commonsense tasks.
• GSM8K (Math Reasoning): Superior results in multi-step problem-solving.
• CommonsenseQA: Strong comprehension of everyday reasoning tasks.

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Ethical Considerations

• Transparency: Responses are structured for verifiability through tagging.
• Bias Mitigation: Includes self-critique to minimize biases and ensure fairness.
• Safe Deployment: Users are encouraged to evaluate outputs to prevent harm or misinformation.

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License

This model is distributed under the Apache 2.0 license, allowing users to use, modify, and share it in compliance with the license terms.

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Acknowledgments

Special thanks to:

• Unsloth for accelerated training workflows.
• Hugging Face for their powerful tools and libraries.

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Experience the AetherLlama l, leveraging its structured reasoning and self-improvement capabilities for any task requiring advanced AI reasoning.