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.

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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.

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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.

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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.

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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.

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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)
   

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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)

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Limitations

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

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Contributing

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

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License

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

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Contact

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