Duplicate from Gugu8/Math-Expanded

.gitattributes

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LICENSE

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+Open Data Attribution Training Disclosure License (ODATL‑1.0)
+Version 1.0 — July 2026
+
+A permissive open‑data license requiring attribution and mandatory disclosure of AI training use.
+
+--------------------------------------------------------------------
+SECTION 1 — DEFINITIONS
+--------------------------------------------------------------------
+1.1 “Dataset” refers to the collection of data, files, metadata, annotations, structures, or any other materials distributed under this License, including any updates, subsets, or modified versions.
+
+1.2 “Licensor” refers to the entity or individual who releases the Dataset under this License.
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+1.3 “Licensee” refers to any person, organization, or system that accesses, uses, modifies, redistributes, or incorporates the Dataset.
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+1.4 “Model” refers to any machine learning system, artificial intelligence system, algorithm, statistical model, or computational process trained, fine‑tuned, evaluated, or otherwise developed using the Dataset.
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+1.7 “Derivative Model” refers to any Model whose training data includes the Dataset or any Derivative Dataset.
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+SECTION 2 — GRANT OF RIGHTS
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+SECTION 3 — MANDATORY ATTRIBUTION
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+“This work uses data provided under the Open Data Attribution Training Disclosure License (ODATL‑1.0).”
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+“This model was trained using data provided under the Open Data Attribution Training Disclosure License (ODATL‑1.0).”
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+SECTION 8 — NO WARRANTY
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+8.1 The Dataset is provided “as‑is,” without warranty of any kind, express or implied.
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+9.1 Rights under this License automatically terminate if the Licensee:
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+10.2 Continued use of the Dataset constitutes ongoing acceptance of all terms.
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+SECTION 11 — CONTACT
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+11.1 For permissions beyond this License, contact the Licensor.

README.md

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+---
+license: other
+language:
+- en
+tags:
+- synthetic
+- math
+---
+# Massive Step-by-Step Mathematics Instruction Dataset
+
+## Dataset Description
+
+This is a 60GB, highly knowledge-dense dataset designed to teach Large Language Models (LLMs) rigorous mathematical reasoning. 
+
+Unlike standard math datasets that only provide the final answer, this dataset emphasizes **Chain-of-Thought (CoT)** reasoning. Every single row contains a detailed, step-by-step breakdown of how to arrive at the solution, making it ideal for supervised fine-tuning (SFT) to improve an LLM's logical reasoning capabilities.
+
+## Dataset Structure
+
+The dataset is stored as a single CSV file (`math_dataset.csv`) and contains two columns:
+
+- `prompt`: The mathematical question or instruction.
+- `completion`: The step-by-step reasoning and final answer.
+
+## Mathematical Domains Covered
+
+The dataset contains hundreds of millions of examples spanning 10 core mathematical disciplines:
+
+1. **Arithmetic**: Long division with rigorous verification (quotient and remainder proofs).
+2. **Algebra**: Quadratic equations utilizing the discriminant (handling both real and complex roots).
+3. **Calculus**: Indefinite integration using the power rule, and derivatives using the chain rule.
+4. **Linear Algebra**: 2x2 matrix multiplication with explicit dot-product calculations.
+5. **Geometry**: Pythagorean theorem calculations for right-angled triangles.
+6. **Fractions**: Addition of fractions requiring Least Common Multiple (LCM) calculation and simplification via Greatest Common Divisor (GCD).
+7. **Statistics**: Calculation of mean, population variance, and standard deviation.
+8. **Trigonometry**: Exact trigonometric values derived from the unit circle and special triangles.
+9. **Logarithms**: Evluation of logarithms by converting between logarithmic and exponential forms.
+
+## License
+Open Data Attribution Training Disclosure License (ODATL‑1.0)

math_dataset.csv

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