ruggsea/Llama3-stanford-encyclopedia-philosophy-QA
ruggsea/Llama3-stanford-encyclopedia-philosophy-QA is an 8 billion parameter Llama 3 model fine-tuned by ruggsea using QLoRA. It specializes in answering philosophical questions with a formal and informative tone, leveraging the Stanford Encyclopedia of Philosophy-instruct dataset. This model is designed to act as an expert philosophy professor, providing rigorous yet accessible explanations for complex philosophical concepts. Its primary use case is for educational or research applications requiring detailed philosophical discourse.
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Model Overview
This model, ruggsea/Llama3-stanford-encyclopedia-philosophy-QA, is an 8 billion parameter Llama 3 variant that has been fine-tuned using QLoRA. Its core purpose is to serve as an expert in philosophical discourse, providing detailed and accurate answers to philosophical questions.
Key Capabilities
- Philosophical Question Answering: Specifically trained on the Stanford Encyclopedia of Philosophy-instruct dataset, enabling it to address a wide range of philosophical inquiries.
- Formal and Accessible Tone: Designed to emulate an "expert and informative yet accessible Philosophy university professor," ensuring responses are both rigorous and understandable.
- Llama 3 Architecture: Built upon the robust
meta-llama/Meta-Llama-3-8Bbase model, inheriting its strong language understanding and generation capabilities.
Training Details
The model was trained with a specific system prompt: "You are an expert and informative yet accessible Philosophy university professor. Students will pose you philosophical questions, answer them in a correct and rigorous but not to obscure way." Key training hyperparameters included a learning rate of 0.0002, a total batch size of 32, and 3 epochs of training.
Good For
- Educational platforms requiring philosophical explanations.
- Research tools for exploring philosophical concepts.
- Applications needing a knowledgeable and articulate AI for philosophy-related queries.