File size: 4,441 Bytes
c2950a1 eafbd79 c2950a1 eafbd79 c2950a1 eafbd79 c2950a1 eafbd79 c2950a1 eafbd79 c2950a1 eafbd79 c2950a1 eafbd79 c2950a1 eafbd79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
---
base_model:
- Meta/LLama3.1-8B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- Llama3
- trl
- COT
- Reasoning
license: apache-2.0
language:
- en
datasets:
- Daemontatox/LongCOT-Reason
metrics:
- accuracy
- character
- competition_math
- code_eval
library_name: transformers
new_version: Daemontatox/AetherLlama
---
# AetherLlama
- **Developed by:** Daemontatox
- **License:** Apache 2.0
- **Finetuned Using:** [Unsloth](https://github.com/unslothai/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.
---
## 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.
---
## Training Details
- **Frameworks:**
- [Unsloth](https://github.com/unslothai/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.
---
## 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.
---
## 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.
---
## 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.
---
## Acknowledgments
Special thanks to:
- [Unsloth](https://github.com/unslothai/unsloth) for accelerated training workflows.
- Hugging Face for their powerful tools and libraries.
---
Experience the **AetherLlama l**, leveraging its structured reasoning and self-improvement capabilities for any task requiring advanced AI reasoning. |