kth8/Llama-3.3-8B-Instruct-SuperGPQA-Classifier
kth8/Llama-3.3-8B-Instruct-SuperGPQA-Classifier is an 8 billion parameter instruction-tuned model, fine-tuned by kth8 on the SuperGPQA dataset. This model specializes in classifying problems into specific disciplines, fields, and subfields, providing structured JSON outputs. It is optimized for precise categorization tasks, leveraging its Llama-3.3-8B-Instruct base for enhanced reasoning in classification.
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Model Overview
kth8/Llama-3.3-8B-Instruct-SuperGPQA-Classifier is an 8 billion parameter model derived from allura-forge/Llama-3.3-8B-Instruct, specifically fine-tuned on the m-a-p/SuperGPQA dataset. Its primary function is to act as a classifier, categorizing problems into predefined disciplines, fields, and subfields.
Key Capabilities
- Precise Classification: Designed to accurately assign input problems to specific categories (discipline, field, subfield).
- Structured Output: Provides classification results in a clean JSON format, making it easy to integrate into automated workflows.
- Optimized for Reasoning: Leverages its instruction-tuned base for robust understanding and categorization of complex queries.
- Low Inference Temperature: Recommended to use with a temperature of 0.0 for deterministic and consistent classification results.
Training Details
The model was trained using PEFT (Parameter-Efficient Fine-Tuning) with a rank of 32 and LoRA alpha of 64, across 2 epochs. It achieved an average training loss of 0.068 and a final validation loss of 0.050, indicating strong performance in its specialized task.
Good For
- Automated content tagging and organization.
- Academic research assistance for categorizing papers or questions.
- Building intelligent routing systems based on problem domain.