Duplicate from prithivMLmods/Gargantua-R1-Compact

Co-authored-by: Prithiv Sakthi <prithivMLmods@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+task_categories:
+- text-generation
+- question-answering
+language:
+- en
+tags:
+- thinking
+- code
+- math
+- MLOps
+- AI
+- science
+- chemistry
+- biology
+- medical
+- Math reasoning
+- Diverse scientific domains
+- Competitive coding
+- Academic science
+- Creative & analytic reasoning
+- MLOps/LLMs/diffusion/CUDA
+- Graphs/charts → JSON
+size_categories:
+- 1M<n<10M
+---
+
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/dN40mA7_MHh4zcvt35QJK.png)
+
+<div style="
+    background: rgba(255, 61, 61, 0.15);
+    padding: 16px;
+    border-radius: 6px;
+    border: 1px solid rgba(255, 0, 0, 0.3);
+    margin: 16px 0;
+">
+
+<details>
+  <summary>Gargantua-R1 Distribution</summary>
+
+![Gargantua-R1-Compact Dataset Distribution - visual selection.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/MMnP1qtx9gqxGyIFOXcQe.png)
+
+</details>
+
+</div>
+
+
+# **Gargantua-R1-Compact(experimental purpose)**
+
+> Gargantua-R1-Compact is a large-scale, high-quality reasoning dataset primarily designed for mathematical reasoning and STEM education. It contains approximately 6.67 million problems and solution traces, with a strong emphasis on mathematics (over 70%), as well as coverage of scientific domains, algorithmic challenges, and creative logic puzzles. The dataset is suitable for training and evaluating models in mathematical problem solving, competitive programming, scientific computing, and reasoning competence.
+
+---
+
+## Quick Start with Hugging Face Datasets🤗
+
+```py
+pip install -U datasets
+```
+
+```py
+from datasets import load_dataset
+
+dataset = load_dataset("prithivMLmods/Gargantua-R1-Compact", split="train")
+```
+
+---
+
+## Dataset Composition
+
+The dataset is strategically balanced across multiple domains with emphasis on mathematical reasoning:
+
+| Category | Percentage | Description |
+|----------|------------|-------------|
+| **Math reasoning** | 73.93% | Core mathematical problems, proofs, and computational challenges |
+| **Diverse scientific domains** | 12.11% | Physics, chemistry, biology, and interdisciplinary scientific problems |
+| **Competitive coding** | 11.35% | Programming challenges, algorithms, and data structure problems |
+| **Academic science** | 1.37% | Research-level scientific questions and methodology |
+| **Creative & analytic reasoning** | 0.95% | Logic puzzles, analytical thinking, and creative problem-solving |
+| **MLOps/LLMs/diffusion/CUDA** | 0.25% | Machine learning operations and specialized technical content |
+| **Graphs/charts Data -> json** | 0.06% | Data visualization and structured data interpretation |
+
+---
+
+## Dataset Statistics
+
+- **Estimated Total Rows:** 6.67M
+- **Estimated Full Size:** 144GB
+- **Dataset Preview:** 232,530 rows
+- **Preview Size:** 2.23GB
+- **License:** Apache 2.0
+
+## Data Sources
+
+The dataset is constructed from multiple high-quality sources:
+
+- **Reasoning Traces:** Derived from `prithivMLmods/Poseidon-Reasoning-5M`
+- **Mathematical Reasoning:** Homogeneous traces from `nvidia/open-math-reasoning`
+- **Custom Problems:** Additional majorly custom modular problems contributed by [prithivMLmods](https://huggingface.co/prithivMLmods)
+
+---
+
+## Key Features
+
+### Mathematical Excellence
+- **Comprehensive Coverage:** From basic arithmetic to advanced mathematical concepts
+- **Step-by-Step Reasoning:** Detailed solution traces for complex mathematical problems
+- **Multiple Approaches:** Various solution methodologies for the same problems
+
+### High-Quality Reasoning Traces
+- **Structured Solutions:** Problems paired with detailed reasoning steps
+- **Verification:** Solutions include validation and checking mechanisms
+- **Educational Value:** Explanations suitable for learning and understanding
+
+### Diverse Problem Types
+- **Pure Mathematics:** Algebra, calculus, geometry, number theory, and discrete mathematics
+- **Applied Mathematics:** Statistics, probability, optimization, and mathematical modeling
+- **Scientific Applications:** Physics problems, chemistry calculations, and biological modeling
+- **Coding Challenges:** Algorithm implementation, data structures, and computational thinking
+
+---
+
+## Use Cases
+
+### Primary Applications
+- **Mathematical Reasoning Model Training:** Fine-tuning LLMs for mathematical problem-solving
+- **STEM Education:** Supporting educational AI systems and tutoring applications
+- **Research & Development:** Advancing reasoning capabilities in AI systems
+- **Competitive Programming:** Training models for coding competitions and algorithmic challenges
+
+### Secondary Applications
+- **Scientific Computing:** Supporting scientific research and computational science
+- **Academic Assessment:** Automated grading and problem generation systems
+- **Reasoning Evaluation:** Benchmarking and testing reasoning capabilities
+
+---
+
+## Dataset Structure
+
+The dataset follows a structured format with the following key components:
+
+```json
+{
+  "problem": "Problem statement or question",
+  "solution": "Detailed step-by-step solution with reasoning traces",
+  "category": "Domain classification (math_reasoning, coding, etc.)",
+  "difficulty": "Problem complexity level",
+  "source": "Origin of the problem"
+}
+```
+
+## Response format:
+
+```py
+<think>
+-- reasoning trace --
+</think>
+-- answer --
+```
+
+---
+
+## Quality Assurance
+
+### Data Curation
+- **Expert Review:** Problems vetted by domain experts
+- **Solution Verification:** All solutions mathematically verified
+- **Format Consistency:** Standardized structure across all entries
+- **Duplicate Removal:** Comprehensive deduplication process
+
+### Reasoning Quality
+- **Pedagogical Value:** Solutions include educational explanations
+- **Multiple Verification:** Cross-checking of mathematical solutions
+- **Error Correction:** Systematic error identification and correction
+- **Clarity Standards:** Clear, understandable reasoning traces
+
+## Getting Started
+
+### Installation and Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("prithivMLmods/Gargantua-R1-Compact")
+
+# Access different splits
+train_data = dataset['train']
+print(f"Dataset size: {len(train_data)}")
+
+# Example usage
+for example in train_data.take(5):
+    print("Problem:", example['problem'])
+    print("Solution:", example['solution'])
+    print("---")
+```
+
+---
+
+## Recommended Use
+- **Fine-tuning:** Use for mathematical reasoning model fine-tuning
+- **Evaluation:** Benchmark mathematical reasoning capabilities
+- **Research:** Academic research on reasoning and problem-solving
+- **Education:** Support educational AI applications
+
+## Performance Benchmarks
+
+Models trained on Gargantua-R1-Compact show significant improvements in:
+- Mathematical problem-solving accuracy
+- Step-by-step reasoning quality
+- Multi-domain reasoning transfer
+- Competitive programming performance
+
+---
+
+## Contributing
+
+We welcome contributions to improve the dataset quality:
+- **Problem Submission:** Submit high-quality mathematical problems
+- **Solution Review:** Help verify and improve existing solutions
+- **Error Reporting:** Report any errors or inconsistencies
+- **Domain Expansion:** Contribute problems in underrepresented areas
+
+## Citation
+
+<div style="
+    background: rgba(255, 61, 61, 0.15);
+    padding: 16px;
+    border-radius: 6px;
+    border: 1px solid rgba(255, 0, 0, 0.3);
+    margin: 16px 0;
+">
+
+<details>
+  <summary>Gargantua-R1-Compact</summary>
+
+
+```bibtex
+@misc{prithiv_sakthi_2025,
+	author       = { Prithiv Sakthi },
+	title        = { Gargantua-R1-Compact (Revision 522d6fb) },
+	year         = 2025,
+	url          = { https://huggingface.co/datasets/prithivMLmods/Gargantua-R1-Compact },
+	doi          = { 10.57967/hf/6176 },
+	publisher    = { Hugging Face }
+}
+```
+
+</details>
+
+</div>
+
+---
+
+## Limitations and Considerations
+
+### Known Limitations
+- **Language:** Primarily English-language content
+- **Domain Bias:** Heavy emphasis on mathematical reasoning
+- **Cultural Context:** Problems may reflect specific educational contexts
+- **Complexity Range:** Varying difficulty levels within categories
+
+### Ethical Considerations
+- **Educational Integrity:** Should not be used to complete academic assignments
+- **Bias Awareness:** Users should be aware of potential biases in problem selection
+- **Responsible Use:** Follow academic and research ethics guidelines
+
+---
+
+## License
+
+This dataset is released under the Apache 2.0 License, allowing for both commercial and non-commercial use with proper attribution.
+
+## Version History
+
+- **v1.0:** Initial release with 6.67M reasoning traces
+- **Preview:** 232K sample for evaluation and testing
+
+| Maintained by | Last Updated |
+|---------------|--------------|
+| **[prithivMLmods](https://huggingface.co/prithivMLmods)** | **Aug 2025** |

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