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  ---
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- base_model: unsloth/qwq-32b-preview-bnb-4bit
 
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  tags:
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  - text-generation-inference
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  - transformers
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  - unsloth
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- - qwen2
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  - trl
 
 
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  license: apache-2.0
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  language:
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  - en
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Uploaded model
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- - **Developed by:** Daemontatox
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- - **License:** apache-2.0
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- - **Finetuned from model :** unsloth/qwq-32b-preview-bnb-4bit
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- This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
 
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- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ base_model:
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+ - Meta/LLama3.1-8B-Instruct
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  tags:
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  - text-generation-inference
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  - transformers
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  - unsloth
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+ - Llama3
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  - trl
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+ - COT
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+ - Reasoning
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  license: apache-2.0
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  language:
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  - en
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+ datasets:
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+ - Daemontatox/LongCOT-Reason
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+ metrics:
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+ - accuracy
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+ - character
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+ - competition_math
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+ - code_eval
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+ library_name: transformers
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+ new_version: Daemontatox/AetherLlama
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+ pipeline_tag: text-generation
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  ---
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+ ![image](./image.webp)
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+ # AetherLlama
 
 
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+ - **Developed by:** Daemontatox
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+ - **License:** Apache 2.0
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+ - **Finetuned Using:** [Unsloth](https://github.com/unslothai/unsloth), Hugging Face Transformers, and TRL Library
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+ ## Model Overview
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+
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+ 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.
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+
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+ ### System Prompt and Workflow
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+
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+ This model operates using an innovative reasoning framework structured around the following steps:
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+
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+ 1. **Initial Thought:**
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+ The model uses `<Thinking>` tags to reason step-by-step and craft its best possible response.
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+ Example:
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+
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+ 2. **Self-Critique:**
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+ It evaluates its initial response within `<Critique>` tags, focusing on:
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+ - **Accuracy:** Is it factually correct and verifiable?
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+ - **Clarity:** Is it clear and free of ambiguity?
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+ - **Completeness:** Does it fully address the request?
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+ - **Improvement:** What can be enhanced?
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+ Example:
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+
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+ 3. **Revision:**
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+ Based on the critique, the model refines its response within `<Revising>` tags.
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+ Example:
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+
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+ 4. **Final Response:**
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+ The revised response is presented clearly within `<Final>` tags.
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+ Example:
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+
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+ 5. **Tag Innovation:**
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+ When needed, the model creates and defines new tags for better structuring or clarity, ensuring consistent usage.
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+ Example:
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+
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+ ### Key Features
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+ - **Structured Reasoning:** Transparent, multi-step approach for generating and refining answers.
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+ - **Self-Improvement:** Built-in critique and revision ensure continuous response enhancement.
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+ - **Clarity and Adaptability:** Tagging system provides organized, adaptable responses tailored to user needs.
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+ - **Creative Flexibility:** Supports dynamic problem-solving with the ability to introduce new tags and concepts.
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+
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+ ---
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+
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+ ## Use Cases
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+
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+ The model is designed for various domains, including:
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+ 1. **Research and Analysis:** Extracting insights and providing structured explanations.
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+ 2. **Education:** Assisting with tutoring by breaking down complex problems step-by-step.
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+ 3. **Problem-Solving:** Offering logical and actionable solutions for multi-step challenges.
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+ 4. **Content Generation:** Producing clear, well-organized creative or professional content.
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+
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+ ---
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+
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+ ## Training Details
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+
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+ - **Frameworks:**
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+ - [Unsloth](https://github.com/unslothai/unsloth) for accelerated training.
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+ - Hugging Face Transformers and the TRL library for reinforcement learning with human feedback (RLHF).
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+
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+ - **Dataset:** Finetuned on diverse reasoning-focused tasks, including logical puzzles, mathematical problems, and commonsense reasoning scenarios.
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+
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+ - **Hardware Efficiency:**
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+ - Trained with bnb-4bit precision for reduced memory usage.
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+ - Optimized training pipeline achieving 2x faster development cycles.
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+
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+ ---
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+
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+ ## Performance Metrics
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+
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+ The model excels in reasoning benchmarks:
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+ - **ARC (AI2 Reasoning Challenge):** High accuracy in logical and commonsense tasks.
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+ - **GSM8K (Math Reasoning):** Superior results in multi-step problem-solving.
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+ - **CommonsenseQA:** Strong comprehension of everyday reasoning tasks.
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+
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+ ---
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+
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+ ## Ethical Considerations
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+
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+ - **Transparency:** Responses are structured for verifiability through tagging.
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+ - **Bias Mitigation:** Includes self-critique to minimize biases and ensure fairness.
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+ - **Safe Deployment:** Users are encouraged to evaluate outputs to prevent harm or misinformation.
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+
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+ ---
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+
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+ ## License
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+
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+ 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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+
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+ ---
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+
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+ ## Acknowledgments
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+
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+ Special thanks to:
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+ - [Unsloth](https://github.com/unslothai/unsloth) for accelerated training workflows.
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+ - Hugging Face for their powerful tools and libraries.
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+
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+ ---
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+
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+ Experience the **AetherLlama l**, leveraging its structured reasoning and self-improvement capabilities for any task requiring advanced AI reasoning.