SuperGrok

SuperGrok Multi-Source Tuning Benchmark

📊 SuperGrok SFT Mixture

A 939k-sample dataset for training SuperGrok models, blending diverse prompts from math, code, and dialogues under ODC-BY-1.0 license. This dataset is meticulously curated to ensure a wide range of applications, from educational tools to advanced conversational AI.

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🧠 Dataset Composition

Includes 18 sources like CoCoNot (10k), FLAN v2 (89k), and Aya (100k), covering varied tasks for robust AI fine-tuning. Each source is selected to enhance the model's ability to handle complex queries and provide accurate, context-aware responses.

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✅ Licensing

Licensed under ODC-BY-1.0, with subsets like No Robots (CC-BY-NC-4.0) and Aya (Apache 2.0), some non-commercial. This ensures that while the dataset is accessible for a wide range of uses, certain restrictions apply to maintain ethical standards and copyright compliance.

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✔️ Data Structure

Each sample has an ID, messages (user prompts + responses), and source, optimized for instruction-tuning. This structured approach allows for efficient training and fine-tuning of AI models, ensuring high performance and reliability.

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📚 SuperGrok Models

Powers Llama-3.1-based SuperGrok series (8B/70B) across SFT, DPO, and RLVR stages for advanced AI performance. These models are designed to push the boundaries of AI capabilities, offering solutions that are not only powerful but also adaptable to various industries and applications.

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🔍 Additional Insights

Explore how the SuperGrok SFT Mixture is revolutionizing AI training with its comprehensive dataset. From enhancing machine learning algorithms to improving natural language understanding, this dataset is a cornerstone for future AI developments.

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🤝 Community and Support

Join a growing community of AI researchers and developers. Access forums, documentation, and expert support to maximize the potential of the SuperGrok SFT Mixture in your projects.

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