CriticalThinker-llama-3.1-8B-GGUF

Overview

CriticalThinker-llama-3.1-8B-GGUF is a fine-tuned version of the LLaMA 3.1 model, hosted on Hugging Face. It is designed to handle critical thinking tasks with advanced reasoning, inference generation, and decision-making capabilities. Leveraging a custom critical thinking dataset, this model excels at structured analysis, logical deduction, and multi-step problem-solving.


Model Features

  • Base Model: LLaMA 3.1, 8 Billion Parameters.
  • Format: GGUF (GPT-Generated Unified Format) optimized for inference.
  • Purpose: General-purpose critical thinking tasks requiring logical reasoning, structured analysis, and decision-making.
  • Training Data: Fine-tuned on a synthetic dataset focused on diverse reasoning scenarios and inference challenges.
  • Reasoning Capabilities: Multi-step deduction, hypothesis testing, and recommendation generation.

Model Applications

  • Problem Solving: Address logical puzzles, hypothetical scenarios, and analytical challenges.
  • Decision Support: Evaluate options and propose well-reasoned conclusions.
  • Structured Analysis: Analyze arguments, identify assumptions, and detect logical inconsistencies.
  • Educational Tool: Enhance teaching materials for logic, philosophy, and structured problem-solving.
  • Research Assistance: Aid researchers in hypothesis testing and developing structured frameworks.

Dataset

This model was fine-tuned on a custom critical thinking dataset that includes:

  • Logical Puzzles: Multi-step reasoning problems requiring sequential logic.
  • Decision Trees: Scenarios for evaluating choices and their outcomes.
  • Hypothetical Cases: Simulated real-world dilemmas to test inference and reasoning.
  • Question-Answer Pairs: Structured prompts with detailed explanations and reasoning steps.
  • Metadata Tags: Problem categories, complexity levels, and reasoning steps.

Performance Benchmarks

Evaluation Metrics:

  • Reasoning Accuracy: 94.5% on logical reasoning tasks.
  • Inference Generation: 92.1% correctness in multi-step problem-solving.
  • Logical Coherence: 90.8% consistency in explanations and conclusions.

Installation

Requirements

  • Python 3.8 or later.
  • Transformers Library (HuggingFace).
  • GGUF-compatible inference tools such as llama.cpp or ctransformers.

Steps

  1. Clone the model repository from Hugging Face:
    git clone https://huggingface.co./theeseus-ai/CriticalThinker-llama-3.1-8B-GGUF
    cd CriticalThinker-llama-3.1-8B-GGUF
    
  2. Install dependencies:
    pip install transformers
    pip install ctransformers
    
  3. Download the model weights:
    wget https://huggingface.co./theeseus-ai/CriticalThinker-llama-3.1-8B-GGUF/model.gguf
    
  4. Run inference:
    from transformers import pipeline
    model = pipeline('text-generation', model='model.gguf')
    prompt = "Analyze the following problem and provide a logical conclusion..."
    result = model(prompt)
    print(result)
    

Usage Examples

Logical Deduction Example

prompt = "A man needs to transport a fox, a chicken, and a bag of grain across a river. He can only carry one item at a time. How does he ensure nothing is eaten?"
result = model(prompt)
print(result)

Decision Analysis Example

prompt = "Evaluate the benefits and drawbacks of remote work in terms of productivity, work-life balance, and team collaboration. Provide a structured conclusion."
result = model(prompt)
print(result)

Limitations

  • May require additional fine-tuning for highly specialized tasks.
  • Performance depends on prompt design and clarity.
  • Ethical use required—intended for constructive applications.

Contributing

We welcome contributions! Submit pull requests or report issues directly on our Hugging Face repository.


License

Licensed under the Apache 2.0 License. See LICENSE for more details.


Contact

For support, contact us via Hugging Face or email *[email protected].

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